CMBES Proceedings

Experimental Study on Surface Quality of Hole and Biological Damage in Bone in Drilling

2023-03-01T10:49:01-08:00

Drilling of bone with a hard metallic drill is a common surgical procedure used in various contexts in orthopedics, neurosur-gery, and dentistry. The performance of the process is based on the minimal invasion to the delicate bone tissue. Surface quality of the drill bit, the drilled hole and biological damage (death of bone cells) are the inevitable outcomes associated with the drilling process. Repeated use of surgical drills and other processes such as irrigation with saline solution and sterilization process causes wear of the cutting edges of the drill which can seriously affect its performance during operation. The aim of this study was to move a step forward towards minimally invasive surgical procedures in bones by investigat-ing the effect of wear of surgical drill bits on postoperative outcomes. The surface quality of the drill was found to influ-ence the surface of the drilled holes as well as the extent of biological damage around the drilling region. Worn drill pro-duced poor surface quality of the hole and caused more cells death near the drilling region compared to a sharp drill.

A strategy to improve image quality of low-dose synchrotron radiation CT imaging for tissue engineering applications

2023-03-08T16:01:52-08:00

I. INTRODUCTION

Hydrogel scaffolds made from biomaterials are used to facilitate cell growth and tissue regeneration and are essential in tissue engineering applications. Hydrogel scaffolds have very low density and synchrotron radiation X-ray computed tomography (SR-CT) shows high contrast for hydrogel scaffolds characterization [1]. However, the radiation dose is of a potential risk using SR-CT for high-resolution imaging in vivo [2]. Reducing the radiation dose has been challenging since low-dose results suffer from noise and artifacts, thus significantly degrading image qualities.

Improving the image quality for low-dose CT imaging has drawn considerable attention over the past decades. Deep learning (DL) methods can help to denoise and remove artifacts; however, most DL-based methods are trained in the supervised mode. Their success critically depends on a large number of paired high-quality data, which typically results in relatively high doses. Such high-quality data are sometimes limited or even impossible to be obtained in biomedical in vivo studies.

We present a low-dose imaging strategy that combines paired high-flux sparse-view CT scan (HF-SV) and low-flux full-view CT scan (LF-FV) based on generative adversarial network (GAN), namely Sparse2Noise. The task is to determine the parameters in network ,

where and are the HF-SV and LF-FV reconstructions, respectively. Sparse2Noise fills the sparse-view artifacts but doesn’t learn the low-dose noise on target.

II. EXPERIMENTS AND RESULTS

We first evaluated Sparse2Noise on the data that were captured from the 3% w/v alginate hydrogel tissue scaffolds in vitro by means of the propagation-based imaging CT (PBI-CT) technique, one of SR-CT techniques. The PBI-CT imaging was performed at the BMIT 05ID-2 beamline, Canadian Light Source, Canada, at a sample-to-detector distance of 1.5 m (30 keV and pixel size of 13 µm). LF-FV images (0.8 Gy) were reconstructed from 1500 projections with 12-cm neutral density filters (NDF). HF-SV images (1.4 Gy) were reconstructed from 75 projections without placing NDF.

Results in Fig. 1 show the noise and ring artifacts are clearly present on LF-FV and HF-SV images, and scaffolds are hard to be identified. By introducing Sparse2Noise, the noise and artifacts can be significantly reduced, thus enhancing the image quality. To produce a similar quality without Sparse2Noise, the dose would be increased to 28.32 Gy.

This study paves the way for in vivo visualization of hydrogel scaffolds using SR-CT with low radiation dose.

Fig. 1 Low-dose SR-CT data of hydrogel scaffolds; (a-c) processed results without and with Sparse2Noise, and (d) the high-dose results as reference.

ACKNOWLEDGMENTs

This work is supported by the Natural Sciences and Engineering Research Council of Canada (Grant numbers: RGPIN 06007-2019 and RGPIN 06396-2019).

REFERENCES

[1] L. Ning et al., "Noninvasive Three-Dimensional In Situ and In Vivo Characterization of Bioprinted Hydrogel Scaffolds Using the X-ray Propagation-Based Imaging Technique," ACS Appl. Mater. Interfaces, vol. 13, no. 22, pp. 25611-25623, 2021.

[2] K. D. Harrison et al., "Direct Assessment of Rabbit Cortical Bone Basic Multicellular Unit Longitudinal Erosion Rate: A 4D Synchrotron‐Based Approach," J. Bone Miner. Res., vol. 37, no. 11, pp. 2244-2258, 2022.

Automated segmentation of knee MR images for biomechanical modeling of the knee joint

2023-03-13T16:42:35-07:00

I. INTRODUCTION

Subject-specific finite element (FE) modeling has provided insight into the cartilage mechanics of normal and pathological knees. Subject-specific FE modeling may someday be used as a diagnostic tool to customize care and enhance therapeutic interventions, but it is currently a time-consuming process, owing in large part to the segmentation of geometry from biomedical imaging.

Here, we propose an automated segmentation method for FE model geometry, where a 3D U-Net convolutional neural network (CNN) and a statistical shape model (SSM) are combined to extract the distal femur and proximal tibia from MR images of the Osteoarthritis Initiative (OAI) database (https://nda.nih.gov/oai/). The validity of the automated approach was verified by comparing FE-predicted maximum principal stress between manual and automated segmentations.

II. Method

First, the femur and tibia were segmented using a 3D U-Net. The outcome was adjusted for improved segmentation using a SSM that included translation, rotation, and scaling ¹. The cartilages were segmented using a 3D U-Net only. The neural network has a 5-level architecture with 16, 32, 64, 128, and 256 (bottleneck) channels respectively at each level. In total, the network has 23 convolutional layers. We used the dice loss as the loss function, i.e., 1 - dice score, and the dice score as the metric for validation. A total of 507 MR images (61.87±9.33 years; 29.27±4.52 BMI [kg/m²]; 0.36×0.36×0.7 image resolution [mm]) from the OAI database with publicly available masks² were used for training, validation and test sets (365, 92 and 50 MR images, respectively). Articular cartilage was extruded from the bone surface using the cartilage thickness from the 3D U-Net. The resulted geometry was meshed using hexahedral elements. Articular cartilage was described using a biphasic constitutive model consisting of an incompressible fluid phase and a fibril-reinforced solid/matrix phase. Bones were considered isotropic homogeneous elastic. A compressive force was applied to the femur, while the tibia was held fixed. The FE-predicted maximum principal stress in the cartilage between the two segmentation methods was compared.

III. Results

The Dice similarity index for femur and tibia were 98±0.3% and for the cartilages was 88±3%. The manual construction of bones and cartilage for each subject took 8 h, as opposed to 10 min using the automated segmentation. The time history of the maximum principal stress from the two FE models developed using either the manual or automated segmentation were compared. The stress magnitude and distribution were very close and statistically insignificant (p > 0.05). Previous FE studies of the knee joint using automated segmentation methods examined only cartilage and ignored bones, while our method considered both bone and cartilage. Previous automated segmentation methods for creating biomechanical modeling of the knee joint were image intensity dependent, while our method eliminated this dependency by using a SSM for geometry adjustment in case of intensity artifacts in an MR image.

IV. conclusion

The combination of CNN and SSM is a novel method for automated segmentation and geometry development of patient-specific FE models of the knee joint. This method relies on both intensity (CNN) and coordinate (SSM) data of the region of interest, resulting in stress distributions from FE analysis comparable to the model from the manual segmentation.

REFERENCES

1 Cootes, T. F. et al. Computer vision and image understanding 61, 38–59 (1995)

2 Ambellan, F. et al. Med Image Anal 52, 109–118 (2019)

First complete anatomical analysis of the entire cochlea at a sub-millimeter resolution using synchrotron-radiation phase-contrast imaging

2023-03-16T15:52:34-07:00

The cochlea is the spiral-shaped end organ of hearing within the inner ear which contains sensory hair cells responsible for transducing mechanical sound vibrations to the electrical impulses we perceive as sound. When the sensory hair cells in the cochlea are not functioning, known as sensorineural hearing, cochlear implants can be used to restore hearing. Cochlear implants are surgically implanted neural-prosthetic devices which directly stimulate auditory nerve fibers, bypassing the hair cells, to restore sound perception in cases of sensorineural hearing loss. However, current cochlear implant electrodes are short and do not stimulate the entire cochlea due to the lack of anatomical knowledge on fine intracochlear structures. The current work scans nineteen human cadaveric cochleae using synchrotron-radiation phase-contrast imaging (SR-PCI) and presents anatomical measurements of the entire cochlea, including the largest inscribed circle and the cross-sectional area at various degrees in the cochlea. These preliminary measurements will extend current anatomical cochlear knowledge to inform safe implantation of longer electrode arrays to restore low-frequency ranges.

Segmentation of retinal layers on OCT scans using deep learning

2023-02-28T12:29:30-08:00

This past decade, the use of artificial intelligence, and more precisely deep learning, has been really efficient in image processing to obtain good performances in object detection and image segmentation. In the medical field, retinal imaging represents an important area of research with a great clinical interest. Indeed, the observation of the retinal layers is helpful in the diagnosis, treatment and monitoring of plenty of retinal pathologies. In this context, this project was focused on using deep learning for OCT retinal layers segmentation. To do so, a UNET-VGG16 model has been employed and the method was evaluated on a Duke OCT database of 4780 B-scans. It succeeded in segmenting three retinal layers with an IoU of 0.529 and a Dice coefficient of 0.685. To go further, the use of data augmentation, pre-processing and post processing functions could solve some issues and improve the method.

Investigation of the Heterogeneous Mechanical Properties of the Intact and GAG-depleted Thoracic Aortic Tree using Indentation

2023-03-15T14:35:33-07:00

1.INTRODUCTION

To assess the risk of lethal aortic complications such as those caused by aneurysms and dissections [1], current clinical evaluation tools are essentially based on anatomical dimensions. As such, they ignore the heterogeneity of the aorta, which may play an important role, and they have inherent limitations that negatively impact the quality of patient care. Recent efforts have focused on deciphering the influence of the aortic composition and its mechanical functions. The extracellular matrix (ECM) of the aorta is obviously of prime interest, as a mixture of elastin, collagen and ground substance such as glycosaminoglycans (GAG) [2]. The purpose of this study was to evaluate the regional mechanical properties of the aorta along its tree using indentation, and most importantly, to evaluate the effect of glycosaminoglycans (GAG) on the tissue’s mechanical response.

2. METHODS

Five porcine thoracic aortas were acquired from a local abattoir and cleaned from surrounding fatty tissue. Three aortic strip samples were extracted from each of the ascending, aortic arch, and descending thoracic regions. One sample was tested under fresh conditions, the second sample served as a control, and the third sample underwent enzymatic GAG depletion. A 100mM ammonium acetate buffer, pH 7.0, was used for control and GAG-depleted samples. GAG depletion was ensured using 15U/mL hyaluronidase, 0.075U/mL chondroitinase ABC, 0.75U/mL heparinase for 48 hours at 37^oC.

The compressive properties were evaluated using a commercially available mechanical tester: Mach-1 Biomomentum (Biomentum Inc., Laval, QC, Canada). Indentation mechanical testing was performed using a 150-gf load cell along with a 1-mm diameter indenter. The sample was placed in a sample chamber filled with phosphate buffered saline at room temperature and was strained to 20% of its thickness from the intimal layer, after 40 preconditioning cycles. Analyses were conducted at 10% strain.

The efficiency of GAG removal treatment was evaluated by quantifying GAG levels after weighing samples and digesting them in papain digestion buffer. A dimethylmethylene blue spectrophotometric assay was used to quantify GAG.

3. RESULTS AND CONCLUSION

We first confirmed that the treatments did not alter the tissue response by comparing the properties of fresh and control samples, as no significant difference was found. The stress at 10% indentation (from intima) was significantly higher in the ascending region compared to the arch and descending thoracic regions for all fresh, control and GAG-depleted tissue. These findings confirm that the aorta is biomechanically heterogeneous along its tree, and more specifically, that the ascending region exhibited higher compressive stiffness compared to the arch and descending thoracic regions. In addition, GAG-depleted samples in the ascending region exhibited a significantly stiffer response compared to the fresh and control samples. However, this was not the case in the arch and in the descending thoracic regions. These findings suggest that the compressive stiffness of the aorta is influenced by the presence of GAG, and that more work is needed to decipher the mechanism of interaction between GAG and other ECM constituents, in order to better understand their influence on compressive stiffness.

ACKNOWLEDGEMENTs

This work was supported by the Natural Sciences and Engineering Research Council of Canada.

REFERENCES

Martufi, Giampaolo, et al. "Is there a role for biomechanical engineering in helping to elucidate the risk profile of the thoracic aorta?." The Annals of thoracic surgery 101.1 (2016): 390-398.
Ghadie, Noor M., Jean-Philippe St-Pierre, and Michel R. Labrosse. "Intramural Distributions of GAGs and Collagen vs. Opening Angle of the Intact Porcine Aortic Wall." Annals of Biomedical Engineering 50.2 (2022): 157-168.

Volatile sputum biomarkers can monitor the response to treatment of nontuberculous mycobacteria disease: a pilot study

2023-03-24T15:37:46-07:00

Nontuberculous mycobacteria disease can cause severe comorbidity and high mortality. Tracking treatment response and determining treatment endpoint remains a major challenge in the clinical management of NTM disease. The current approach for monitoring treatment response requires multiple cultures and radiographic results, which is time-consuming and relatively insensitive. Here we report nine biomarkers selected by comparing paired pre-treatment (n=6) and post-treatment (n=6) sputum samples. The suggested biomarkers can distinguish the pre-treatment group from the post-treatment group. The results demonstrate that detecting volatile sputum biomarkers is a potential supplementary tool for monitoring the response to the treatment of NTM disease.

Artificial Intelligence Education for Medical Students: A Systematic Review

2023-03-08T17:49:13-08:00

Introduction: In the recent literature, several groups have advocated for the incorporation of artificial intelligence (AI) training and literacy into medical curricula to prepare future physicians for its use. [1- 3] Currently, there is no uniform curriculum incorporated into medical training, which may be ascribed to obstacles such as the selection of topics with the proper breadth and depth. This systematic review seeks to identify and compile the existing evidence-based recommendations as key steps towards an AI curriculum in undergraduate medical education.

Methods: MEDLINE, EMBASE, CINAHL, ERIC, NCBI, and Web of Science were searched from database inception to May 2022 for articles addressing AI education in undergraduate medical education (UGME). The search terms of “medical education”, “artificial intelligence”, “medical curriculum”, and “medical program” were used and combined with boolean operators “AND”, “OR”, and “ADJACENT”. The inclusion and exclusion criteria for this study were determined a priori; studies about UGME with fair quality or higher using the Newcastle-Ottawa scale were included. A thematic analysis was performed to identify core themes.

Results: The original search yielded 991 studies after duplicates were removed. After title, abstract, full-text screening, and reference mining, 38 studies were included for analysis. The studies were separated into two categories: survey (n = 18) and interventional (n = 21). A thematic analysis identified six themes: ethics (n = 11, 28.9%), theory and application (n = 15, 39.5%), communication (n = 11, 28.9%), collaboration (n = 7, 18.4%), quality improvement (n = 9, 23.6%), and perception and attitude (n=3, 7.9%). Within ethics, subthemes of patient and data ethics emerged. Theory and application was further divided into knowledge needed for practice and for development. Communication was stratified as being for clinical decision-making, for implementation, and for knowledge dissemination.

Conclusion: Overall, the six identified themes could serve as a useful framework in building a comprehensive AI curriculum for UGME. Future work on the implementation and integration of the themes into UGME curricula is required.

Novel technique for simultaneous imaging of the breast stiffness and incompressibility using quasi-static elastography

2023-03-07T18:16:00-08:00

I. INTRODUCTION
Breast cancer is one of the most common cancers,
representing 25% of all new cancers and 13% of all cancer
related deaths in Canadian women [1]. Early detection before
treatment of breast cancer is paramount as survival rates decrease significantly over time. Some of the most common diagnostic and screening procedures include breast MRI, mammography, and manual examination. These methods are
either too costly or have difficulty detecting or differentiating
malignant tumors from benign ones without a follow-up biopsy. One technique that has shown a potential to minimize
the number of biopsy cases is ultrasound elastography (USE),
which images the breast stiffness which is known to be substantially different for normal and pathological tissue [2].
Currently, the images produced by USE tend to be
of low quality, plagued by noise and distortions due to the
nature of ultrasound, inconsistent mechanical stimulation by
the operator, and other inconsistencies in acquisition or tissue
structure. We have developed new real-time techniques
aimed at improving the data quality by enforcing known
physical properties [3]. This work aims at developing a
method by which the Young’s modulus, shear modulus, and
Poisson’s ratio are simultaneously reconstructed.

II. PROPOSED METHODS
The reconstruction algorithms follow an iterative
technique that include stress calculation followed by
Young’s modulus and shear modulus reconstruction based on
Hooke’s law where both axial and lateral strains are utilized.
The Poisson’s ratio is then constructed using the two moduli
images. By running these algorithms on a dataset of several
in-silico models of tissue deformation at different loading
levels, along with clinical breast cancer cases, we analyzed
the accuracy, signal to noise ratio, and contrast to noise ratio
of the stiffness images to determine which algorithms are
more suited to breast cancer diagnosis.

III. RESULTS
The results of both in-silico and clinical cases show that,
measured by their high signal-to-noise ratio and contrast-tonoise ratio, the reconstructed images of Young’s modulus,
shear modulus and Poisson’s ratio are of high quality. Each
image shows some unique features. For example, while the
Young’s modulus measures tissue stiffness the shear modulus can be used to assess bonding between adjacent types of
tissue. The latter shows low shear modulus at the outline of
benign lesions.

IV. CONCLUSION
The investigation shows promising capability of the
proposed algorithms to produce high quality images of tissue
Young’s modulus, shear modulus and Poisson’s ratio that
showed complementary features that can be fused for accurate breast cancer diagnosis. Further investigation is necessary to measure the method’s sensitivity and specificity.

Deep Learning Model for COPD Classification/Staging Using Lung CT

2023-03-14T11:37:11-07:00

Chronic Obstructive Lung Disease (COPD) is a progressive and prevalent lung disease which is associated with airflow obstruction due to inflamed airways and lung parenchyma. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) utilizes a combined assessment of COPD using three factors of lung function, symptoms, and exacerbation history to evaluate disease severity and prevent future risk through devising treatment tailored according to the three assessment factors. In this study, we developed a deep learning 3D-CNN model using patient’s thoracic CT images instead of the data pertaining to lung function to assess COPD severity based on a high-resolution 8-stage system. The developed COPD classification/staging system has demonstrated high accuracy of 83% in assessing COPD severity using the 8-staging COPD scheme, providing highly valuable diagnostic information useful for treatment planning.

Selecting the Most Characteristic Vestibular Stimuli to be Used for Alzheimer’s Subtype Diagnosis

2023-02-28T17:37:37-08:00

This study aimed to find the most effective tilts in electrovestibulography (EVestG) to differentiate Alzheimer's disease (AD) from Alzheimer's disease with cerebrovascular disease pathology (AD-CVD) using principal component analysis (PCA). EVestG records responses to physical stimuli (tilts), and the goal was to rank these in terms of their ability to separate AD from AD-CVD. The study analyzed EVestG signals from 28 AD and 24 AD-CVD individuals. PCA was used to determine the mean contribution of tilts to the first 26 principle components, which represent 81% of the data variation. The algorithm was tested on 80% of a randomly selected database and found that the Supine Up/down and (sitting) Up/down tilts, which predominantly stimulate the utricle and saccule respectively, were the most effective in separating AD from AD-CVD

Optimization of Intracellular Genetic and Non-genetic Cargo Delivery using Clinical Ultrasound and Microbubbles

2023-03-10T15:34:45-08:00

I. INTRODUCTION
Targeted delivery of drugs, genes and siRNA using ultra-sound and microbubbles (USMB) has emerged as a promis-ing approach for treating diseases such as cancer and acute respiratory distress syndrome (ARDS).1 Optimization of ultrasound settings and microbubble concentration is criti-cal for achieving high efficiency of intracellular cargo delivery. However, currently there are no systematic opti-mization studies investigating how different clinical ultra-sound system settings and microbubble parameters affect the efficiency of genetic and non-genetic cargo delivery. In this work, we show how different clinical ultrasound set-tings and microbubble concentration affect intracellular delivery of model drugs such as fluorescent dextran and model genetic cargoes such as fluorescent siRNA and plasmids using a novel, 3D-printed, modular platform for USMB studies.

II. METHODS
For systematic optimization of different cargo delivery parameters, a standardized, 3D-printed, modular platform called ultrasound-microbubble-cell chamber (UMCC) is developed.1 UMCC is designed on the concept of modulari-ty and consists different pre-designed blocks that snap-fit into each other to form the complete platform, analogous to LEGO® blocks. We use a commercial Phillips SONOS 5500 ultrasound system with Phillips S3 transducer, as well as commercial DEFINITY® microbubbles for optimization studies. As a model drug, we use 4 kDa FITC dextran and 70 kDA tetramethylrhodamine dextran. We also use Alexa Fluor 488 tagged-siRNA as well as green-fluorescent pro-tein (GFP) plasmids as model genetic cargoes. We study the effect of different clinical ultrasound parameters, spe-cifically mechanical index (MI), pulse interval (PI), ultra-sound exposure time as well as DEFINITY® microbubble concentration on efficiency of cargo delivery. MI is varied from 0.1 to 1.3, PI is varied from 200 ms to 5000 ms, ultra-sound exposure time is varied from 20 seconds to 80 sec-onds and microbubble concentration is varied from 8 x 105 bubbles/mL to 2.4 x 109 bubbles/mL. Experiments are done with HEK293 and CMT167 cell lines and cargo delivery is quantified using flow cytometry.

III. RESULTS
We discovered that the efficiency of cargo delivery in-creases with increasing MI, with highest delivery efficien-cy obtained at MI of 1.3. Cargo delivery was observed to decrease with increasing PI with highest delivery efficien-cy at PI of 200 ms. Moreover, we found that the cargo delivery increased 3-fold when ultrasound exposure time is increased from 20 seconds to 40 seconds. Increasing expo-sure time beyond 40 seconds did not significant change the efficiency of cargo delivery. We also found that cargo delivery peaks in a narrow range of DEFINITY® mi-crobubble concentration, ranging from 4 x 107 to 1.6 x 108 bubbles/mL.

IV. CONCLUSION
Our results show that different parameters such as MI, PI, exposure time and microbubble concentration affect intracellular delivery of cargo using USMB-delivery and these parameters can be optimized for maximizing the efficacy of cargo delivery in vitro. These optimum parameters could be potentially used for maximizing delivery of cargo in in vivo animal models.

ACKNOWLEDGEMENTS
We thank Monika Lodyga from the Research Core of Keenan Research Centre for Biomedical Science for help with flow cytometry. We also acknowledge funding from Natural Sciences and Engineering Research Council (NSERC).

REFERENCES
1. K. Joshi, R. Sanwal, K. Thu, S.S.H. Tsai and W.L. Lee, “Plug and Pop: A 3D-Printed, Modular Platform for Drug Delivery Using Clinical Ultrasound and Microbubbles,” Pharmaceutics, 14(11), 2516, 2022.

Knee Joint Mechanics Predicted by Subject-specific and Generic Models

2023-03-01T12:14:31-08:00

INTRODUCTION

Osteoarthritis (OA) affects the life quality of approximately 7.2 million Canadians (18.3%) with an additional 200,000 new cases each year [1]. Knowledge of joint contact mechanics is needed to understand mechanical factors in OA progression. Patient specific knee joint modelling has been well developed over decades. The geometry of knee tissues, on the other hand, varies substantially among individuals, and these morphological variances may have significant implications in injury and disease risk [2]. In order to capture the morphological variability distributed across a set of matching surfaces, our going research uses statistical shape modelling (SSM) method and a unified material to isolate the effects of patient demographics in a large population via modelling to discover generic and subject-specific biomechanical behaviors. The research objectives of the present study were to develop a generic knee model based on our previous developed poromechanical knee joint model and to investigate the contact mechanics including viscous effect of articular cartilages for a subpopulation.

Method

Two generic knee models were generated using a previously developed SSM workflow [3]. Generic39 was generated from the right knees of 39 healthy subjects (45-69 years, white male) and Generic8 was generated from 8 knees randomly selected from the 39 subjects. Tissue geometries of each knee were reconstructed and meshed from MRIs obtained from the Osteoarthritis Initiative. The SSM approach used the Coherent Point Drift algorithm to establish node correspondence between individuals’ point clouds for the bones, cartilages, and menisci. Following alignment of the point clouds, PCA was applied to the registered knee joint data to extract the principal modes of geometric variation. Cartilages and menisci were modelled as fibril-reinforced fluid-saturated materials using a previously developed constitutive model [4]. To model articular cartilage creep response, we simulated a full extension joint load for each generic cohort and selected subject specific models. Joint loading consisted of a 600-N force ramped in 1s and remained constant for 6000s.

Results

The maximum fluid pressure in medial tibial cartilages at 1s of cohort averaged and single subject comparison are shown in Figure 1.

Fig. 1 Maximum fluid pressure in the medial tibial cartilages with creep loading for the generic and subject-specific knee joints.

DIscussion

We used 39 right knees to investigate the viability of SSM in poromechanical modelling of the human knee joint. The variation between subject and cohort averaged models was found to be considerable. Inter-subject variability should be included in subject-specific models since the results may differ from the cohort average. The principal modes and more typical subject-specific models will be built to obtain statistical results.

ACKNOWLEDGEMENTs

This research was funded by the Natural Sciences and Engineering Research Council of Canada.

REFERENCES

Bombardler C. et al., The Arthritis Alliance of Canada (2011).
Clément et al, Computer methods in biomechanics and biomedical engineering, 20.1 (2017): 94-103.
Bruce O.L et al., Comput Methods Biomech Biomed Eng 25: 875-86 (2022).
Gu KB and Li LP, Medical engineering & physics 33.4 (2011): 497-503.

Investigating the relationship between prefrontal cortex oxygenation and locomotor muscle oxygenation during incremental exercise using near-infrared spectroscopy

2023-02-03T15:25:01-08:00

Monitoring prefrontal cortex oxygenation (PFCO) during exercise has been of paramount importance as a measure of decision-making and cognitive responsibilities. Near-infrared spectroscopy (NIRS) is an optical technique that can measure and monitor tissue oxygenation of the brain and skeletal muscles in real-time. The objective of this study was to investigate the pattern of cerebral oxygenation during progressive exercise and compare it with exercising muscle oxygenation at the anaerobic threshold (AT). Healthy adults with moderate to high fitness levels participated in an incremental exercise protocol on a stationary bicycle. Two wearable NIRS sensors were used to monitor NIRS measures of tissue oxygenation from the forehead and the vastus lateralis (VL) muscle. A metabolic cart was used to monitor respiratory gas exchanges. Respiratory Exchange Ratio (RER) > 1.0 was used to estimate the AT. After the AT was reached, the oxygenated hemoglobin (O₂Hb) concentration in the VL decreased significantly (p<0.05). However, there was no significant change in O₂Hb in the cerebral cortex after the AT (p>0.05). Our data demonstrated that while exercising muscle oxygenation decreased at higher intensities after the AT, cerebral oxygenation did not decline significantly. This study introduced NIRS as a noninvasive optical technique to monitor the prefrontal cortex and muscle oxygenation in real-time, making it particularly well suited to exercise sciences.

A computational framework to model the lifecycle of a breakthrough neurovascular implant: crimping into catheter and deployment mechanisms

2023-03-14T11:20:14-07:00

Percutaneous treatment of cerebral aneurysms (CAs) has recently gained the attention of researchers and practitioners. The advent of the eCLIPs implant (product of Evasc Neurovascular Enterprises, Vancouver, Canada) has revolutionized the percutaneous treatment of CAs by offering innovative solutions to the challenges pertinent to other neurovascular devices, i.e. excessive vessel injury caused by device and artery interaction and blocking the daughter vessels in bifurcation cases [1]–[3]. However, in a subset of bifurcation CAs with fusiform pathology, eCLIPs fails to provide sufficient neck bridging, where a gap exists between the device structure and the aneurysm/artery wall upon device deployment. We have developed a new design for the eCLIPs (VR-e) by making the length of device ribs variable to cover such an inflow gap [2]. In this study, we have developed a new finite element model to evaluate the device behavior during crimping into a catheter and its expansion at the aneurysm neck, which is not possible by testing a new device for the endovascular application experimentally.

Exploring walking entrainment with vertical force oscillations

2023-03-24T16:56:17-07:00

Exploring walking entrainment with vertical force oscillations

R.T. Schroeder1, J.L. Croft¹ and J.E.A. Bertram2

¹Kinesiology, University of Calgary, Calgary, Canada

²Biomedical Engineering, University of Calgary, Calgary, Canada

INTRODUCTION

In walking, the legs mediate interactions between the individual’s body mass and the substrate. How would the control policy change if that interaction were artificially altered? In this study, we impart vertical force oscillations via a body harness and alter frequency or amplitude (in real time) during two experiments. Under certain circumstances subjects match steps to the oscillation frequency and this defines a “Basin of Entrainment” [1] for external oscillations.

Methods

A custom mechatronics system imparted periodic forces to subjects walking on a treadmill [2]. The system used electric motors to pull up and down on a body harness, where frequency and amplitude were prescribed, depending on the experiment.

In the “Sensitivity” experiment (Fig. 1A), constant-frequency oscillations (different from the subject’s baseline step frequency) were prescribed, starting at low amplitude and gradually increasing to 30% Body Weight (BW). In the “Range” experiment (Fig. 1B), constant-amplitude oscillations were prescribed, starting at the subject’s baseline frequency and gradually drifting away before eventually returning. In both experiments, the amplitudes and frequencies where subjects synchronized their steps to the oscillations determined a “Basin of Entrainment”.

Results & Discussion

Subjects entrained more consistently with high-amplitude oscillations (Fig. 1C). However, they also entrained at lower amplitudes (increased sensitivity) when oscillations were closer to baseline step frequencies. Interestingly, subjects also entrained more with oscillation frequencies below baseline versus above. Treadmill speed was constant, so entrainment to lower frequencies required an increase in stride length, and subjects appeared more amenable to this adjustment. Regardless, some subjects still eventually entrained to frequencies as much as 9% above baseline, but this required amplitudes approaching 30% BW. Entrainment range also

Fig. 1 A) Sensitivity experiment with varied amplitude, constant frequency. B) Range experiment with varied frequency, constant amplitude. C) Basin of Entrainment for subjects per amplitude/frequency combinations.

increased with oscillation amplitude and with frequencies below baseline.

These results allow us to construct a “Basin of Entrainment” (Fig. 1C) indicating the range and sensitivity of individuals to vertical force oscillations. This study can help inform rehabilitation strategies where, for example, entrainment motivates patients to relearn faster walking speeds during recovery from stroke or other neuromuscular injuries.

REFERENCES

[1] J. Ahn and N. Hogan, “The Basin of Entrainment of Human Gait Under Mechanical Perturbation,” in ASME Dynamic Systems and Control, Ann Arbor, Michigan, USA, Oct. 2008.

[2] R. T. Schroeder, J. L. Croft, and J. E. A. Bertram, “Evaluating the energetics of entrainment in a human–machine coupled oscillator system,” Sci Rep, vol. 11, no. 1, p. 15804, Dec. 2021, doi: 10.1038/s41598-021-95047-x.

Smart hydrogel probes to measure complex tissue mechanics within engineered tumors

2023-02-14T23:23:20-08:00

Abstract—Measuring the rate of expansion of dispersible hydrogel microspheres embedded within engineered tumors is used to assess highly local viscoelastic tissue characteristics. We find that viscoelasticity is both heterogenous and closely correlated with invasive programs in breast cancer models.

Clinical Relevance— establishes a potential diagnostic modality for metastatic risk via dynamism of tissue architecture.

Blood flow restriction therapy: The essential value of accurate surgical-grade tourniquet autoregulation

2023-02-15T09:41:05-08:00

Blood flow restriction (BFR) therapy, in which arterial blood flow into a subject’s limb is restricted by a pressurized tourniquet cuff during prescribed therapy, has been shown to induce significant improvements in muscle strength, hypertrophy and endurance. However, differences in equipment and methodology have led to inconsistent restrictions of blood flow (‘inconsistent BFR pressure stimuli’). This prevents meaningful comparisons of results and identification of optimal therapy protocols and outcomes. We conducted a pilot study to evaluate the ability of five common BFR systems to accurately maintain and autoregulate the actual BFR cuff pressure near target BFR pressure throughout prescribed exercise periods. For effectiveness and safety, accurate autoregulation for BFR was defined to be the same as for surgical tourniquet systems: automatic and rapid self-regulation of cuff pressure to within ±15 mmHg of the target pressure, within one second in the presence of transient pressure changes associated with exercise. Fifteen subjects (8 male; 7 female) completed a standard 30/15/15/15 BFR protocol at 2-second eccentric and 2-second concentric cadence on a horizontal leg press for each BFR system. Target pressures followed manufacturers’ recommendations and actual BFR cuff pressures were recorded at 100 samples per second for the duration of BFR exercise periods. The percentage of time BFR systems provided accurate surgical-grade autoregulation of BFR cuff pressure were: 36.6% ± 41.9% (B Strong), 100% ± 0.0% (Delfi), 45.2% ± 33.6% (Saga), 35.3% ± 34.6% (Smart Tools), and 62.1% ± 26.7% (Suji). At the end of prescribed exercise periods, actual BFR cuff pressures in 3/5 systems differed from target pressures among subjects by more than 15 mmHg and 4/5 systems demonstrated standard deviations of more than 15 mmHg. In view of the pilot results, it is recommended that BFR systems having accurate surgical-grade autoregulation be used to achieve consistent, safe and effective BFR therapy.

Visualization of Fibrillar Collagen Networks Using Label-Free Nonlinear Optical Microscopy: Deciphering the ECM Structure of 3D-Bioprinted Constructs

2023-03-03T16:58:13-08:00

Biological tissues are not exclusively composed of cells. A substantial part of their volume is extracellular space, largely filled by an intricate network of macromolecules. Collagens are one of the main components of this complex network, called extracellular matrix (ECM). The ECM serves as the scaffolding for tissues and organs throughout the body, playing an essential role in their structural and functional integrity. Understanding the basic mechanisms involved in the ECM remodelling of tissues provides a window to advance the development of in vitro tissue models and enhance tissue engineering applications. In this study, we leveraged one label-free nonlinear optical microscopy imaging modality with a novel microfluidic-based three-dimensional (3D) bio-printing technology to investigate the fundamental effects of in vitro model design on the cellular ability to remodel fibrillar collagen. Label-free second harmonic generation (SHG) microscopy allowed us to track fibrillar collagen deposition, and our preliminary data indicated that specific geometries allow cells to remodel ECM more efficiently. Additionally, SHG presents the potential to assess the ECM network development over time, providing important quantitative information crucial to inform artificial tissue development strategies further. Being able to correlate the ECM deposition with cellular behaviour, migration, and even responses to treatments, opens the door to enable the biofabrication of complex networks able to mimic a variety of tissue types and pathological conditions.

Frequency Bias in MLM-trained BERT Embeddings for Medical Codes

2023-03-09T14:49:26-08:00

Transformers are deep networks that operate on loosely structured data such as natural language and electronic medical records. Transformers learn embedding vectors that represent discrete inputs (e.g. words; medical codes). Ideally, a transformer should learn similar embedding vectors for two codes with similar medical meanings, as this will help the network make similar inferences given either of these codes. Previous work has suggested that they do so, but this has not been analysed in detail, and work with transformers in other domains suggests that unwanted biases can occur. We trained a Bidirectional Encoder Representations from Transformers (BERT) network with clinical diagnostic codes and analyzed the learned embeddings. The analysis shows that the transformer can learn an undesirable frequency-related bias in embedding similarities, failing to reflect true similarity relationships between medical codes. This is especially true for codes that are infrequently used. It will be important to mitigate this issue in future applications of deep networks to electronic health records.

Video-Based Face and Facial Landmark Tracking for Neonatal Vital Sign Monitoring

2023-02-16T10:43:03-08:00

This paper explores automated face and facial landmark tracking of neonates, for the purposes of vital sign estimation. Utilising a publicly available dataset of neonates in the clinical environment, 25 videos were annotated. Face and facial landmarks (i.e. eyes and nose) tracking are then assessed. Additionally, the identification and tracking of the neonate's forehead and cheeks are purposed, as they are ideal regions of interest for vital sign estimation. Tracking of the face produced an average overlap score of 93.0%. Tracking of the eye and nose landmarks produced mean normalised errors of 0.026 and 0.019 respectively. The cheek region of interest could be effectively identified and tracked, whereas the forehead region of interest identification was incorrect 16% of the time.

The Vasculens - Projector-Based Augmented Reality Display of Anatomical Structures Segmented from Pre-Operative CT Scans

2023-03-16T15:55:05-07:00

Augmented reality technologies can improve surgical navigation. These augmented reality technologies promise to improve both the safety and efficiency of operations. The Vasculens is a novel handsfree and focus free projector-based augmented reality system that works by projecting segmented preoperative CT scan data directly onto a patient. The Vasculens is designed to help surgeons visualize vascular anatomy during free flap harvesting surgeries. One example of a free flap is a deep inferior epigastric perforator (DIEP) flap that is harvested to create a new breast mound after mastectomy.

This paper reports the reprojection accuracy of the Vasculens. In brief, 25 metal fiducials were placed on the torso of a life-sized mannequin, the mannequins were scanned in a CT scanner, the metal fiducials were segmented, and the Vasculens was used to project the expected location of the metal fiducials onto the mannequins. The reprojection accuracy was defined as the mean of the absolute distance between the location of the metal fiducial and the projected fiducial location. The mean reprojection error for the female mannequin was 0.6 mm for the male mannequin was 0.2 mm. These accuracy results support the conclusion that the Vasculens is a promising technology for improving DIEP flap breast reconstruction surgery and other free flap surgeries.

Can Heart-Rate Monitors Predict Muscle Anaerobic Threshold during Intense Exercise?

2023-02-16T12:47:35-08:00

The point at which lactic acid starts to build up in the muscle during intense anaerobic exercise is known as the anaerobic (AT) or lactic threshold (LT). The goal for many athletes is to increase this threshold, allowing them to perform longer at high intensity by training at or slightly above the AT. Heart-rate monitors and fitness trackers, which have a projected global market size of USD 114 billion in 2028, are used to guide athletes during their training programs to improve their anaerobic capacity. The objective of this study was to investigate the efficacy of heart-rate monitoring in predicting muscle AT during in-tense exercise in relation to the respiratory exchange ratio (RER) and relative body oxygen consumption (VO2) and to investigate new techniques for sports monitoring. Twenty healthy and abled-body adults with moderately to high fitness levels participated in an incremental exercise protocol consisting of 5-minute intervals on a stationary spin bike. An electrocardiogram (ECG) chest sensor was used to measure and monitor the heart rate, while a metabolic cart was used to measure and monitor RER and VO2. RER > 1.0 was used to determine the AT, which was compared to the heart rate data. It was observed that there was no indication of a consistent anaerobic heart rate zone to determine AT for all participants (p > 0.05). In Figure 1, a comparison and common trend is shown between RER and heart rate, where the heart rate displays a linear trajectory despite RER indicating AT at Step 4. This suggests that heart rate monitoring is an insufficient means for predicting AT and that there is a need for more advanced wearable monitoring technologies that can monitor and detect changes in exercising muscle metabolism that are associated with AT.

Fusion of Manual and Deep Learning Analyses for Automatic Lung Respiratory Sounds Identification in Youth

2023-02-28T12:14:01-08:00

Lung sounds contain important clinical information which can be used for identifying respiratory and/or lung disorders. Manual identification of respiratory events is time-consuming and prone to subjective errors. While several automatic respiratory event classification techniques have been proposed previously, they are mostly focused on the identification of respiratory sounds in the adult population. Though, this is challenging in youth as lung is developing till the age of 20 years old which affects the parameters of respiratory sounds. In this research, our goal is to develop techniques for respiratory sound classification in youth using the SPRSound dataset, which includes recordings of individuals from 0-18 years old. The objectives include binary and multi-class classification of respiratory events (objective 1) and recordings (objective 2). For objective 1, we extracted purified respiratory features using a convolutional neural network (CNN) as well as frequency and time domain features, statistical features, and patient demographics, while for objective 2, a mixed model of long short-term memory (LSTM) network and a gradient boosting classifier with a novel voting scheme is developed. The features which were significantly associated with the different respiratory sounds were used to train machine-learning models for classification purposes. We evaluated the models’ performance based on sensitivity, specificity, an average of sensitivity and specificity scores, and the F1-score. The final performance score is defined as the average of the AS and F1- score. Our proposed framework reached 0.91±0.03 and 0.82±0.03 in binary and multiple event classification, respectively. Also, the developed model reached 0.74±0.02 and 0.55±0.03 in ternary and multi-class recording sound classification. Finally, the performance of the overall framework is calculated based on the grand challenge definition and our proposed pipeline reached 0.74.

Deep Learning Model for OSA Detection using Tracheal Breathing Sounds During Wakefulness

2023-02-28T11:01:37-08:00

The detection of Obstructive Sleep Apnea (OSA) during sleep is a simple and well-established technique; however, its detection during wakefulness is challenging. In this paper, we propose a deep learning model for the detection of OSA using only the tracheal breathing sounds spectrum as input. We employed our team’s previous dataset consisting of 109 subjects as non or mild-OSA with apnea/hypopnea index (AHI) < 15 and 90 subjects as OSA with AHI ≥ 15. All study subjects were referred to overnight polysomnography (PSG) to determine their AHI values. Tracheal breathing sounds were recorded in the supine position before proceeding to PSG while awake. The recording protocol was to have 5 deep breaths first through the mouth and then 5 deep breaths through the nose. Data were normalized and segmented into inspiratory/expiratory breathing phases; their power spectra were then calculated and fed to a deep learning model consisting of 71 layers. The results of 10 K-fold show that the proposed deep learning model achieved an accuracy of 74.9%, sensitivity of 76.1%, and specificity of 73.3%. Although these results are not as high as the previously reported analyses, they can be improved significantly by combining with anthropometric parameters and subgrouping subjects based on their age, weight, etc. This work aimed to show the potential of deep learning on this dataset despite the limited sample size. The results are encouraging to continue and improve the algorithm.

Two-stage cultivation: An innovative method for augmented bioactive metabolites production from euryhaline microalgal species for their utilization in nutraceuticals

2023-03-15T11:42:22-07:00

Microalgae have the excessive adaptation strategy to survive in diverse physicochemical or stress conditions, where they struggle for nutrients and space and endure by producing several bioactive metabolites (BM’s) in their body. BM’s accumulation in microalgae can be prompted by various abiotic stress conditions. Two-stage cultivation for augmentation of BM’s production could be an effectual approach in euryhaline microalgal species. In this way, they are firstly cultivated in nutrient-adequate optimized conditions to make the most of biomass, whereas, in the next stage, salinity-cum-alkalinity stresses persuade the accretion of desired BM. In this study, euryhaline microalgal species Spirulina subsalsa, Scenedesmus MKB, BGLR7, BGLR8, and BGLR18 collected from waterlogged areas of Punjab, India was identified and mass multiplied under optimized and two-stage cultivation conditions. Microalgal species were grown under optimized conditions up to 20 days after subculturing (DAS) and then transferred to salinity-cum-alkalinity stress and allowed to grow for 10 more days. At 30DAS stage, biomass was harvested, freeze-dried, and evaluated for biochemical constituents viz., proteins, lipids, carbohydrates, total Kjeldahl nitrogen, and phenolic compounds i.e., bound phenolics, free phenolics, bound flavonoids and free flavonoids, and DPPH radical scavenging activity. In comparison to the one-stage cultivation i.e., under optimized conditions, two-stage cultivation showed a significant upsurge in BM’s accumulation signifying their delightful defense method to tolerate salinity-cum-alkalinity. We reported a significant increase in antioxidant activity under two-stage cultivation. Furthermore, due to augmented BM’s production, two-stage cultivation for euryhaline microalgal species could perfectly be a good way for the production of novel nutraceuticals and dietetic supplements. Moreover, we are looking forward to the development of molecular techniques for strain perfection of euryhaline microalgal species by genetic engineering.

Unsupervised Learning Using Time-Domain and Frequency-Domain Features of Audio Signals for the Classification of Mild Cognitive Impairment

2023-02-28T11:43:31-08:00

In this research, we conducted a study on MCI detection with unsupervised learning. We collected a total of 104 audio samples from Mandarin-speaking test subjects. The study sample contains 72 MCI patients and 32 normal test subjects diagnosed by a clinical psychiatric doctor. Time-domain and frequency-domain features of the mid- and short-term audio signals are extracted and their accuracy performances are analyzed. With unsupervised learning based on frequency-domain features, the accuracy of MCI detection can reach 73%.

Reconstruction of the Stress-free Hyperelastic Parameters of Breast Tissue: Machine-Learning Based Inverse Problem Technique

2023-02-28T11:56:08-08:00

INTRODUCTION

A key to simulate the breast deformation using the finite element method (FEM) is the accurate mechanical properties of its tissue. The breast tissue is known to undergo large deformation under loading pertaining to medical intervention. As such, its properties are best described by hyperelastic parameters. Breast tissues exhibit significant deformation even under sole gravity loading experienced by the tissue in the preloading phase of traditional mechanical testing techniques. These techniques ignore this initial stressed state, leading to inaccurate estimates of the tissue hyperelastic parameters, hence impacting estimated breast deformation. In the context of computer assisted medical diagnosis and intervention such inaccuracies may translate into inaccurate diagnosis or ineffective intervention. To address this issue, a robust method is necessary to estimate the tissue stress-free hyperelastic parameters using their counterpart obtained under gravity loading.

Methods

We propose a machine-learning based inverse-problem solution to convert hyperelastic parameters of the breast obtained from conventional mechanical testing [1,2] to their stress-free counterparts. In this study we investigated this conversion for the Yeoh and 1st order Ogden models. For this purpose, each hyperelastic parameter reported in the literature is scaled down incrementally down to 50% of its value. To construct a data space of the stress-free parameters, various combinations of the scaled down parameters are formed after checking the Drucker Stability condition, leading to over 800 points. For each point in this space, a uniaxial test is simulated using FEM where the gravity preloading is included, to obtain simulated stress-strain data. This data is fitted in accordance with the hyperelastic model to estimate the corresponding hyper-elastic parameters under gravity preloading conditions. This will lead to two data spaces of hyperelastic parameters, one in the stress-free and the other in the gravity preloading states. To map the latter space to the former, we construct a neural network (NN) that contains three layers with 80 hidden neurons in each layer. Once this NN is trained, it can be used to convert any breast tissue hypereleastic parameter set obtained in traditional mechanical testing to its stress-free state counterpart.

Results

We calculated the distance between predicted and true unloaded hyperelastic parameter points, and used the r2 parameter to measure the accuracy of results obtained from the NN. For the two independent models: Yeoh and 1st Ogden, the best predicted accuracy was obtained at 0.91 and 0.86, respectively.

Conclusion

The proposed method is capable of predicting the stress-free hyperelastic of the breast tissue using their loaded counterpart with high accuracy. While only two hyperelastic models were investigated, the method can be adapted with other models and to other types of tissues (e.g brain and liver).

REFERENCES

Samani A, Plewes D. A method to measure the hyperelastic parameters of ex vivo breast tissue samples. Phys Med Biol. 2004 Sep 21;49(18):4395-405.
Dempsey SCH, O'Hagan JJ, Samani A. Measurement of the hyperelastic properties of 72 normal homogeneous and heterogeneous ex vivo breast tissue samples. J Mech Behav Biomed Mater. 2021 Dec;124:104794.

An Electrochemically-Active Biosensor to Study the Development of Biofilm in Wild-Type and Fimbriae- Deficient E. coli Mutants

2023-03-25T09:36:06-07:00

Biofilms are clusters of bacterial aggregates, encased in a self-produced matrix of extracellular polymeric substances and adherent to the surface. Biofilms pose significant consequences in medical settings, associated with 80% of microbial and 60% of all infections, originating in a hospital. A standard technique for biofilm determination is through crystal violet staining. An irreversible as-say, it produces inconsistent responses in the case of different mutants or antimicrobial agents. To address this, a novel biosensor has been developed to investigate E. coli biofilm life cycle by selecting wild-type (WT) and strains without fimbriae (genes involved in biofilm formation – fimC and fimD) mutants.

Fabrication of different forms of chitosan-coated alginate fibers on a single microfluidic platform

2023-03-05T10:55:50-08:00

Fabrication of different forms of chitosan-coated alginate fibers on a single microfluidic platform

INTRODUCTION

For decades, researchers have used various tools to fabricate tissue-like biological constructs. Fibers make up most of human tissues, such as the extracellular matrix. The most common methods of creating fibers are electrospinning and microfluidic spinning. Since microfluidics does not involve a high voltage, the process is safe and allows the loading of cells, drugs, and proteins inside fibers without damage.

Alginate and chitosan are selected as the biopolymers in this study. Alginate is a popular natural hydrogel for fiber fabrication because of its low cost and high biocompatibility. Alginate and chitosan are commonly used together due to their electrostatic association.

Many techniques have been reported for fabricating alginate fibers. The solidification of fibers inside microfluidic channels makes fiber extrusion challenging. In addition, other methods require adjusting the device design to achieve different forms of fibers.

Aqueous two-phase systems (ATPS) have been used in numerous applications. To address the challenges outlined above, microfluidics is combined with ATPS. We introduce a spacer phase between alginate and crosslink agents to prevent immediate contact. Depending on the thickness of the spacer phase, the crosslinking agent must diffuse for a certain distance before reaching the alginate phase. As a result, we can control the rate of solidification. Moreover, ATPS allows us to fabricate different forms of fibers by adjusting only the inlet pressures.

Methods

The microfluidic platform is made using the standard soft lithography method. We utilize ATPS based on dextran and poly(ethylene) glycol as the base fluids. The ATPS phase separates into a dextran-rich (DEX) phase and a poly(ethylene) glycol-rich (PEG) phase, and generally, we utilize PEG and DEX as the basis of the fiber and continuous phases, respectively. We also dissolve 1.5% alginate in the innermost PEG phase of the microfluidic device. The innermost PEG phase is cladded by a DEX phase, which is then surrounded by an outer PEG phase that contains 2% barium chloride. Fibers form once the barium chloride from the outer PEG phase diffuses across the intermediate DEX phase and gel the innermost alginate-PEG phase. We modulate the pressure profiles in each inlet and change the ATPS fluids in the inlets to create solid, hollow, and droplet-filled fibers. Fibers are soaked for an hour in a 1% chitosan solution to form a thin chitosan coating around the fibers.

Results

We obtain experimental results from different pressure profiles at each inlet, and different ATPS phases at each inlet, to obtain a phase diagram that indicates regimes leading to different types of fibers. Increasing the pressure of the first two inlets transitions the fiber-forming core fluid from backflow, to droplet-filled single threads, and then to continuous single threads. We can co-flow the two innermost fluids in the device by altering the pressure profile and ATPS of the inlets. We also alter the overall fiber diameter by adjusting the pressure of the innermost inlet. Once cross-linked, continuous single threads, continuous co-flow threads, and droplet-filled threads, become solid, hollow, and droplet-filled fibers.

Conclusion

This technique enables the fabrication of different forms of chitosan-coated fibers by using only one platform. In the future, we anticipate loading drugs into fibers, and applying the fibers to various tissue engineering applications.

REFERENCES

M. Jeyhani, R. Thevakumaran, N. Abbasi, D. K. Hwang, S.S.H. Tsai, “Microfluidic Generation of All-Aqueous Double and Triple Emulsions”. Small 2020, 16 (7), 1906565.
K.Y. Lee, D.J. Mooney, “Alginate: Properties and biomedical applications”. Prog Polym Sci. 2012;37:106–26.

Automatic evaluation of the ejection fraction on echocardiography images

2023-03-13T17:53:21-07:00

The ejection fraction measures the amount of
blood pumped by the heart. This study aims at automatically
evaluating the ejection fraction from echocardiography images.
First, a supervised learning model with different deep neural
network architectures was proposed for the segmentation of the
ventricle at the end of diastole and end of systole on echocardiography images. Then, a regression model was designed to study
the difference of the area between the ventricle at the systole and
the diastole, which provide an estimation of the ejection fraction. The model was evaluated on a subset of the EchoNet-Dynamic dataset. The four models described in this article study
the ejection fraction of the hearts with another approach than
the previous EchoNet-Dynamic studies. The results could be
used in future projects to predict heart diseases.

Where? Evaluation of Source-Detector Position in Spatially Resolved Spectroscopy

2023-02-01T00:24:07-08:00

Continuous-wave near-infrared spectroscopy (CW-NIRS) has found clinical use in monitoring tissue oxygen saturation, particularly in continuous monitoring of cerebral oxygenation in surgical patients. Recently, there has been interest in miniaturized, implantable NIRS sensors to directly interface with internal organs such as the spinal cord. This study establishes a starting point in assessing the effects of sensor miniaturization on NIRS-based oxygenation measurements.

Design and Evaluation of a Virtual Reality-based Driving Task to Investigate Temporal Preparation

2023-03-24T16:38:34-07:00

Implicit timing is one of the less investigated components of humen’s time perception. In particular, temporal preparation, which means reduced reaction time to act before an expected event , has not received enough attention in the previous studies. The focus of this study is to design and validate a Virtual Reality (VR)-based driving task to examine this important aspect of implicit timing. During the VR test, the participants were expected to implicitly learn the time between the yellow light and red light being shown to them in the virtual street at three different intervals(i.e. 2, 5, 8s). We hypothesized that the young adults ,with no sex differences, can successfully form their temporal expectancies in our task by showing a significant decline in their reaction times. Participants were 27 young adults (14 females). The average absolute error for each target interval was calculated based on the participants’ reaction times. Wilcoxon Signed rank test and Mann–Whitney U test were used for eliciting the difference between the experimental conditions and sex groups respectively. Our results confirm the significant reduction in absolute errors of the participants' reaction times for longer time intervals(5 and 8 s) with no difference between males and females.

Re-establishing Impact Speed Requirements for Inducing Commotio cordis

2023-03-08T16:08:49-08:00

INTRODUCTION

Commotio cordis (CC) is a sudden death mechanism involving pump failure of the heart from impacts over or near the cardiac silhouette ^[1] and has garnered heavy publicity due to the sporting incidences involving American football player Damar Hamlin and ice hockey player Chris Pronger. While these events took place 25 years apart, they are both unique in the sense that CC occurred in 2 professional athletes with discrete impacts to the heart. It has been established that CC requires low-velocity impacts to the chest ranging from 30 to 50-mph ^[2]. This study investigated the re-establishment of impact speed requirements to induce CC with older age groups and developed bodies of adults.

METHODS

This study administered the Total Human Model for Safety (THUMS), a finite element model of an adult male, to recreate CC inducing impacts. An anonymous survival case of CC involving a 19-year-old male collegiate baseball player was reconstructed and analyzed. Based on recollections and witness analysis, the player was struck directly over the heart with a 90-mph pitch and was not wearing a chest protector. To further understand what was happening to the heart during this impact, we used the AM50 (Adult male 50^th percentile) model with a regulation sized baseball aimed directly over the heart at speeds of 90-mph. Left ventricular strain values from impact were measured.

RESULTS

Strain dispersion seen throughout the adult heart showed large portions of the left ventricle experiencing strain levels upwards of 25%. Approximately 40% of the left ventricular tissue was engulfed in these strain levels (Fig. 1). Left ventricular strain was highest in the epicardium of the left ventricle, suggesting that the electrically disturbed cardiac tissue would likely be found in this region.

Fig. 1 Comparison of AM50 heart during 90-mph baseball impact (left) and 10-year-old heart during 40-mph baseball impact (right), showing peak strain values of the left ventricular region of the heart.

DISCUSSION

Currently, the established requirements for inducing CC state the impact speed must be in the range of 30 to 50-mph. When comparing the strain values from the AM50 model with 90-mph impacts to previous studies using a child 10-year-old model ^[3] with 40-mph impacts, we can see that the strain levels and distribution are strikingly similar (Fig. 1).

This study provides evidence that CC impact speeds of 30 to 50-mph may be specific only to youth, and as individuals get older with a larger and stiffer thoracic cavity, the impact speed requirements for CC increase with age. Therefore, CC is reliant on age, weight, and stiffness of the individual, and should be seen as a spectrum regarding induction impact speeds, rather than absolute values. This is an important case analysis supporting re-establishing of the required speeds for inducing CC, and should influence chest protector design and regulations across all sports regarding the prevention of CC.

REFERENCES

J. Maron et al., "Clinical profile and spectrum of commotio cordis," (in eng), JAMA, vol. 287, no. 9, pp. 1142-6, Mar 2002.
S. Link et al., "An experimental model of sudden death due to low-energy chest-wall impact (commotio cordis)," New England Journal of Medicine, vol. 338, no. 25, pp. 1805-1811, 1998.
J. Dickey et al. “Identifying Vulnerable Impact Locations to Reduce the Occurrence of Deadly Commotio Cordis Events in Children’s Baseball: A Computational Approach,” J Biomech Eng. Vol. 144, no. 5, May 01 2022.

Microfluidic micromixing by micropost-based acoustic microstreaming

2023-03-14T11:18:50-07:00

1. INTRODUCTION

Microfluidic mixing is important in many applications, including biology, chemistry, and drug delivery. In active methods, external forcing creates additional mixing interfaces to enhance molecular exchange. Passive techniques utilize features of the channel geometry to increase mixing. Acoustic microstreaming-based microfluidic mixing is promising but has so far been limited due to high costs and low mixing efficiency[1]. We aim to introduce a novel acoustic mixing concept, which can be very useful for micromixing applications with channels of high aspect ratios (i.e., very wide channels) where other acoustic techniques (wall-based actuators) are much less effective.

2. Materials and methods

We employ acoustics to disturb and thereby mix fluids in a microfluidic device. The microfluidic device is fabricated by soft lithography, and a transducer is attached to the glass substrate of the device to enable acoustic excitation [2]. We create micropillars inside the microchannel using stop-flow lithography. The transducer actuation leads to the vibration of the glass substrate, which in turn, vibrates the posts inside the microfluidic channel such that microstreaming flows are generated around each pillar. The microstreaming flow significantly enhances the fluid mixing inside the channel [3].

3. Results

We have created a robust protocol to examine mixing performance quantitively by designing a setup to avoid flow fluctuations over time and monitor mixing using an inverted microscope. We investigate different parameters in the micropillar-fabrication slit channel lithography process to create various shapes and stiffness microposts. Finally, we also evaluate the resulting mixing performance from these parameter changes.

4. Conclusion

We aim to perform parameter sweeps for transducer power, micropost geometry and stiffness, and microchannel geometry to optimize the microfluidic acoustic micromixing platform. Achieving the goal of optimizing mixing performance will lead to benefits in applications in biology, chemistry, and other fields.

REFERENCES

[1] Li, Z., et al., A review of microfluidic-based mixing methods. Sensors and Actuators A: Physical, 2022: p. 113757.

[2] Salari, A., et al., Dancing with the Cells: Acoustic Microflows Generated by Oscillating Cells. Small, 2020. 16(9): p. 1903788.

[3] Zhao, S., et al., Fabrication of tunable, high-molecular-weight polymeric nanoparticles via ultrafast acoustofluidic micromixing. Lab on a Chip, 2021. 21(12): p. 2453-2463.

Impact of Multiple Responses to a Singles Sonication when Determing Nanobubble Contrast Agent Characteristics

2023-03-10T15:43:51-08:00

Multiple scatter responses were observed in a phantom channel containing nanobubble contrast agent following a single sonication, with agent replacement between trials changing the responses’ relative positioning and intensities. Experimentally, the combined spectrum of several responses can show split peaks instead of a single peak at the sonication’s carrier frequency. This is the result of the real and imaginary components of the complex-valued frequency spectra of individual scatter responses combining in a constructive or destructive manner. In some trials, changes were observed in agent longevity with a time constant of 2.5 milliseconds during multiple sonications. This paper applies two measures to determine whether this longevity variability is real or an algorithmic artefact due to minor position shifts of several responses modifying the way their real and imaginary spectral components interfere. The first power measure determines the spectrum from all phantom data undistorted by reflections from the channel boundaries. The second measure determines the short-term Fourier transform of the hydrophone signal with an analysis window the length of the sonication moved across the full agent response, potentially capturing changes in individual scatter responses. Combining the results from the two metrics confirms that the decrease in agent longevity during multiple sonications is real and not the result of an algorithmic artefact associated with shape changes in the multi-peak spectra during one trial. However, given that the positioning of the multiple responses is uneven across the phantom channel and changes with agent replacement, we conclude that the measure using all available data will be more consistent when used to compare agent-tissue contrast from different imaging schemes.

Using Deep Learning to Estimate Frame-to-Frame Angle Displacements in 2D Ultrasound Image Sequences of an Infant Hip

2023-03-16T15:54:17-07:00

To assess developmental dysplasia of the hip in infants, evaluations are currently conducted based on 2D ultrasound (US) images. Using 3D US has been shown to markedly reduce inter-rater variability, but 3D scanners are not widely available in pediatric practices. Here, we propose using deep learning to estimate the spatial positions of 2D US image sequences; this can then be used to form 3D reconstructions. In this study, we extracted fan-shaped sets of slices from a database of 1403 3D US volumes and trained a previously proposed standard convolutional neural network (CNN) as well as two variations of a deeper CNN (one augmented with optical flow (OF) information) to estimate the angular distances between separated slices. The deeper CNN most accurately predicted the inter-slice angular displacements, with a mean absolute error of 0.02˚, for displacements of up to 3.0˚ (corresponding to a center-frame displacement of 5.3mm). OF did not appear to improve prediction accuracy in angle estimation. The deeper CNN also achieved a mean end-to-end sweep angle error of -0.8% ± 13.2%, compared with an error of 25.3% ± 14.7% for the previously proposed standard CNN. This relatively low error suggests that it may be feasible to accurately reconstruct a 3D representation of an infant hip using a 2D US video stream alone, without requiring additional probe-tracking devices.

MR image prediction at high field strength from MR images taken at low field strength using multi-to-one translation

2023-03-07T18:33:25-08:00

Patients with implants may need to undergo Magnetic Resonance Imaging (MRI) at lower field strengths to avoid negative impacts from the strong electromagnetic fields. However, the quality of low-field MRI images may be inferior, potentially leading to inaccurate clinical diagnoses. To overcome this limitation, our study proposes a convolutional neural network developed by using U-Net to generate high-field MR images from low-field ones. The proposed model employs multiple MRI contrasts at lower field strength to generate MR images in one or several contrasts at higher field strength. This method overcomes the limitations of previous research which only utilized a single contrast (one contrast-to-one contrast translation) or multiple contrasts including MR image at high (target) field strength as input. After creating a dataset for multi contrast-toone contrast translation, the model was optimized using techniques such as data augmentation and selection of the best model with minimum validation loss. The generated MR images were evaluated using metrics such as Mean Squared Error (MSE), Pearson Correlation Coefficient (Corr), and Peak Signal-to-Noise Ratio (PSNR). The results indicate that for predicting T1- and PD-weighted MR images at high field strength, the average range of MSE and PSNR over the test dataset (1392 images) did not result in improvements compared to one-to-one translation, while Corr shows improvement in PD-weighted MR image prediction. Also, the reported results for the average range of MSE and PSNR suggest improvements in high-field T2-weighted MR image prediction using multi-to-one transla-
tion.

Expansion of Human Skin-derived Schwann Cells in Stirred Suspension Bioreactors

2023-03-10T16:52:17-08:00

N/A

Influence of Bone Microarchitecture on the Stressed Volume of Equine Subchondral Bone

2023-03-01T11:47:08-08:00

INTRODUCTION

Musculoskeletal injuries are a significant source of lost training time and morbidity in racehorses. The metacarpophalangeal (MCP) joint is the most frequently injured site and there is a consensus that injuries at this location are a mechanical fatigue phenomenon. The fatigue life of bone is strongly determined by bone microarchitecture and the resulting stress concentration volume [1]. In equine subchondral bone, fatigue life was positively associated with bone volume fraction (BV/TV) in the deep trabecular region [2]. BV/TV is a function of both trabecular number and thickness, suggesting that trabecular bone geometry plays an important role in the fatigue resistance of equine subchondral bone. The objective of this work was to quantify the influence of bone microarchitecture on stress concentration volume in the trabecular region of equine MCP joint subchondral bone.

METHODS

Twelve cylindrical bone samples measuring 6 mm in length and diameter were cut from the MCP joints of three equine forelimbs (4 samples/limb). Samples were scanned using μCT (Scanco Medical AG, Bassersdorf, Switzerland) with an isotropic voxel size of 6 μm and acquisition settings of 55 KVp and 145 μA. Microarchitecture measurements for the deep trabecular region (proximal most 2 mm of sample) were quantified using Fiji (NIH, USA) software. Measurements included BV/TV, trabecular number, trabecular thickness, and ellipsoid factor (the proportion of plate and rod-like trabecular structures). Finite element models were developed from the μCT images to quantify stress concentration volume for each sample using FAIM software (Numerics88 Solutions Ltd., Canada). Elements were assigned homogeneous, linear-elastic material properties. A uniform compressive load of 65 MPa was applied to the subchondral surface and the trabecular surface nodes were fixed. Stressed volume was defined as the volume of material experiencing a stress greater than the yield strength of equine subchondral bone.

RESULTS

BV/TV was strongly correlated with stressed volume (Table 1). Mean trabecular thickness correlated more strongly with stress concentration volume than the number of trabeculae. There was no significant relationship between stressed volume and the proportion of trabecular rods and plates.

CONCLUSION

Stress concentration volume is a known determinant of bone fatigue strength that correlates with BV/TV. This work suggests that trabecular thickness is more important than trabecular number for the fatigue strength of equine subchondral bone.

REFERENCES

L. Loundagin, A.J. Pohl, W.B. Edwards, “Stressed volume estimated by finite element analysis predicts the fatigue life of human cortical bone: The role of vascular canals as stress concentrators,” Bone, 143 (115647), 2001.
S. Martig, P.L. Hitchens, P.V.S. Lee, R.C. Whitton, “The relationship between microstructure, stiffness and compressive fatigue life of equine subchondral bone,” J Mech Behav Biomed Mater, 101 (103439), 2020.

An Automated Online Recommender System for Stroke Risk Assessment

2023-03-07T18:39:01-08:00

A stroke is a complicated emergency event that leads to major neurological impairments and patient disability. It is imperative to have an automated smart recommender system that can help with early stroke prediction or detection and hence assist clinicians in stroke risk management. This study proposes the use of an automated online recommender system that can predict stroke risk levels based on the given patient-specific clinically identified stroke risk factors, such as systolic and diastolic blood pressure, age, gender, smoking habit, and cholesterol level. We integrate this model in an interactive Django-based web framework built upon software engineering best practices that can assist clinicians in monitoring key stroke risk factors dynamically in real-time using the Smartwatch device. We use machine learning (ML) techniques to predict stroke risk levels, and also employ an Explainable Artificial Intelligence (XAI) technique to rank the risk factors to provide meaningful insights for stroke risk management. We also have built a real-time patient monitoring system that can monitor patient vital signals in real-time using a Smartwatch and transmit the data to the web application where the data is concurrently processed by the ML model and can be displayed in an interactive dashboard. The results from the study show that this automated online recommender system can predict stroke risk levels with an average Area Under the Curve (AUC) of 0.98 while providing meaningful insights on the stroke risk factors that can assist clinicians in better managing stroke risk whilst being cost-effective and feasible based on popular Smartwatches and Smartphones.

Wearable microfluidic sweat pH sensor

2023-02-15T20:47:37-08:00

Skin interfaced wearable devices consisting of microfluidic channels, electrochemical and calorimetric assays utilize the sweat as analyte and therefore offer noninvasive and real time physiological information . The biomarkers present in the sweat could be effectively used for comprehending various disorders such as diabetes, cystic fibrosis, and stress as well as monitoring of drugs, pH, and electrolytes.

Major challenges of most of these microfluidic wearable technologies are related to 1) the contamination of old and new sweats, 2) stability of biosensing methods, and 3) variation of sweat pH during the sensing of biomarkers.

Cortisol is the principal biomarker present in the sweat which is directly correlated to stress. Hence real time monitoring of stress could be achieved by measuring the cortisol in the sweat. The real time or longitudinal measurement of stress in diseases like concussion is much needed as stress in these patients can lead to significant health issues and as well as it can be life threatening to them.

One technical challenge of accurate detection of cortisol using biosensors is the dependency of the sensors to pH of solution. Therefore, it is crucial to measure pattern concentration of cortisol. Here we developed simple, low-cost wearable microfluidic chip that measures the sweat pH in real time. This sensor can be easily integrated with cortisol sensors embedded within microfluidic chips.

The Development of Soft Capacitive-based Pressure and Shear Sensor Arrays for Prevention of Pressure Injuries

2023-02-16T09:46:14-08:00

Soft sensors are capable of detecting a variety of physical stimuli such as pressure, shear, proximity, etc. while being actively deformed. These devices open up a plethora of applications in sports, robotics and healthcare. One of the applications can be in the prevention of one of the most burdensome, costly and fatal secondary medical conditions, which are pressure injuries (PIs). This chronic wound develops due to prolonged pressure sufficient enough to occlude the blood supply to tissue regions (ischemia), resulting in ulceration and potentially fatal infections. The effect of pressure can be in combination with shearing forces, causing additional tissue damage. Most susceptible to PIs are individuals with restricted mobility and sensation, such as the spinal cord injury (SCI) and the elderly population, as well as hospitalized/bedridden patients. We developed soft sensor arrays that are flexible and/or stretchable and designed for pressure and shear detection. These sensor arrays are scalable in size and resolution and made out of silicone elastomer material, carbon black particles and fibres, and stretchable, conductive fabric. The working principle is based on capacitive sensing, where electrodes form an array of parallel-plate capacitors separated by a dielectric layer. When compressed or deformed, a relative change in capacitance is measured, which translates to pressure and shear. The sensor arrays demonstrated their capabilities of measuring pressure within the desired range for PIs (0-200 mmHg) for short and long periods with low-repeatability error, creep and accuracy of ±4%, as well as detecting directional shearing forces in 2-dimensional axes up to 1 N. Figure 1 highlights the performance in detecting pressure magnitudes, the repeatability error and creep of the sensor array. The results suggest that the soft sensor arrays have the potential to be successfully used in PI prevention applications. Further work is required in characterizing the sensor arrays, testing them with human subjects in applicable real-life situations, as well as advancing the overall understanding of PI formation and adapting sensor designs to meet the requirements.

Huddle Up: Improving Medical Engineering Safety, Quality and Delivery

2023-03-07T15:22:32-08:00

1. Background

Daily huddles, defined as routine, structured, and brief team discussions, have become prevalent in healthcare as a tactic to enhance safety and achieve status as a high-reliability organization.[1] While the impact of huddles in healthcare is still emerging, their success in other complex industries (e.g., military, nuclear power, aviation) have fostered their rapid proliferation in healthcare given their ability to enhance situation awareness and shared learning.[1] Despite their potential benefits, little is known about how support departments, such as Health Technology Management (HTM) departments, can use daily huddles to augment communication both within the department and the greater hospital. The lack of such insight is a critical gap in knowledge as HTM departments frequently struggle with situation awareness and issue escalation, particularly in large academic hospitals.

In 2017, University Health Network (UHN, Toronto, Canada) launched hospital wide huddles as part of a larger organizational transformation to renew their focus on patient and staff safety. The HTM department (Medical Engineering) was included in this initiative.

2. Proposed Presentation

The overall goal of the proposed presentation is to share how UHN and the Medical Engineering Department has embedded and iteratively refined daily huddles as a critical mechanism to achieve performance goals. More specifically, the presentation will review the following:

UHN organizational huddles:
- Format (e.g., focus on safety, quality, delivery)
- Structure (e.g., issues being progressively escalated up from all units through their respective programs, site leadership and executives).
Medical Engineering team huddles:
- Format
- Structure
- Quality improvement indicators
- Tools and dashboards used to track real-time performance

To help contextualize Medical Engineering huddles, a video of a mock huddle will be shared and highlight how issues are communicated, escalated, tracked, and resolved. Lessons learned and changes made since the launch of daily huddles over 5 years ago will also be reviewed together with a summary of their benefits and limitations/challenges. However, overall, their sustained use has led to improved: learning and relationship building within Medical Engineering and the rest of the hospital; sensitivity to operations (e.g., more timely notification and escalation of safety issues); staff morale (e.g., phycological safety, team building); and performance (e.g., timely repairs and preventive maintenance, problem solving, accountability).

REFERENCES

1. J. Franklin et al, "Impact of multidisciplinary team huddles on patient safety: a systematic review and proposed taxonomy," BMJ Quality & Safety, vol. 29, (10), pp. 1-2, 2020.

Medical Device Layout Guide – Systemic Approach Based on Patient and Medical Device Type

2023-03-07T15:10:52-08:00

The planning guides published by the Quebec Ministry of Health and Social Services (MSSS) greatly facilitate the work of the teams of professionals involved in developing functional and technical projects. Each project having its own particularities, this causes an eternal restart when the time comes to produce the list of medical equipment that will make it possible to establish the level of funding estimated for this project. With the new constructions that have taken place in Quebec in the last decade (CHUM, MUHC, Jewish General Hospital Sir Mortimer B. Davis, CHU of Quebec, etc.), we consider that these projects submitted to the MSSS by each institution have sufficient data to issue guidelines that will accompany current and future development guides for the medical equipment component. Our work consists in demonstrating the feasibility of such an approach by developing a parameterized tool to know the quantity, the location, the attributes, the budget and the installation constraints of each medical device as well as its functional link of planning with other related equipment. This study could be extended to non-medical equipment, furniture and consider estimating the operating costs of these new facilities, including human and material resources. This work is preliminary. We are counting on the collaboration of other health authorities to validate our data and develop a tool that will adapt to standards, technological developments and medical practices.

Women Trailblazers in Clinical and Biomedical Engineering

2023-03-06T13:28:10-08:00

History is paved with innovators in the field of biomedical engineering but most of them are well‑known male figures. This paper will dive into a few women innovators in biomedical engineering and clinical engineering. We will discuss their innovations, what drives them and what impact their discoveries or inventions had in engineering and clinical engineering. We will address the challenges each of the women faced in their field and how they used their influence to make a difference and leave a legacy into their respective disciplines. At the end of this paper, you will find substantial evidence that there are plenty of women innovators in clinical engineering and biomedical engineering, even though they are never taught in classrooms or in history books to convey the existence and many contributions of the women. Also, you will be more motivated to encourage women to enter, excel, and stay in this male-dominated field and share these successful stories. Representation of women innovators matters in society.

Role of Professional Organizations in Promoting Your Biomedical/Clinical Engineering Career

2023-03-07T15:26:28-08:00

A Biomedical/Clinical Engineering professional, whether they are a biomedical technician or a biomedical/clinical engineer needs to know what factors help grow and enhance their career.

Of course education, experience, trainings and certifications matter, but often many fail to recognize the important role professional organizations (e.g. AAMI, CMBES, ACCE and others) play in providing that extra push, guidance and experience that sets them apart as they move ahead in their career.

Joining a professional organization at any level and any time during their career can help the Biomedical/Clinical Engineering professional boost their career.

This presentation will draw upon decades of the experience the presenters had with the professional organizations and how it helped enhance their Biomedical /Clinical Engineering career.

What are we missing? Addressing critical elements of clinical engineering practice in Australia

2023-03-07T17:24:44-08:00

What are we missing? Addressing critical elements of clinical engineering practice in Australia

B.J. Morrison¹and Dr Kerry Newlin²

^1,2 Morrison Newlin Consulting, Candelo, Australia

I. BACKGROUND

Australia has a hybrid model of healthcare with universal basic coverage provided by the commonwealth government with private insurance available for purchase. State-run public hospitals deliver most inpatient care and can be accessed for free by everyone in Australia with a Medicare number.

Clinical engineering (CE) services are provided in all public hospitals. Metropolitan hospitals have in-house CE departments, regional and rural hospitals are typically served from a larger hospital, and some public hospitals utilize contracted private companies.

Fortunately, Australia has no shortage of available skilled CE personnel, appropriate facilities including access to spare parts and consumables and quality service documentation.

Unfortunately, clinical engineering in Australia lacks several critical components to underpin quality and continuous improvement. Missing elements include: a nationwide peer review process, an up-to-date standard of practice (SoP), and specific competency measures for clinical engineers and biomedical engineering technicians (BMETs). A bottom-up approach to identifying and addressing these missing elements of CE practice is underway, driven by several senior clinical engineers eager to see the profession move forward before their retirement.

II. DEVELOPING QUALITY IMPROVEMENT TOOLS

Attempts to develop quality and continuous improvement tools have been initiated by different engineering entities (Fig. 1) over the past 20+ years.

Figure 1 – Learned society structure in Australia

Peer review process: Engineers Australia (EA), the peak body for all engineering disciplines, endorsed a national peer review management model in 2008. Unfortunately, after the pilot phase, the program ended due to lack of funding. One local peer review initiative has been running in five Sydney, New South Wales public hospitals since 2004 but otherwise no statewide or nationwide peer review programs have been implemented to date.

Standards of Practice. In 2008, EA endorsed a CE SoP developed by the Victorian Hospitals Association in collaboration with The Canadian Medical and Biological Engineering Society (CMBES). This document provided the standards for the peer review process but otherwise was underutilized and is no longer contemporary.

The National Committee for Clinical Engineering (NCCE) is currently updating the CE SoPs with expected completion by mid-2023. Collaboration will again be sought with CMBES.

Competency measures. Although EA has well defined generic competencies for benchmarking engineers of all disciplines and all grades, there are no specific competencies for clinical engineering practitioners at either the professional or sub-professional levels.

The NCCE is developing competencies to be assigned for clinical engineers and technicians based on categories of practice (electronics BMET, mechanical BMET, etc.).

III. NEXT STEPS

While several prior attempts to move the profession forward through peer review, standards of practice and competency measures have stalled, momentum is building now.

Once these quality improvement elements are fully developed, the next hurdle will be the adoption and implementation by CE services in all six states and two territories. This may be difficult as there is no governance framework for CE across Australia. The federal system allocates responsibility for health to the states and territories, who at present, see no gain in adopting an Australia wide system of peer review with associated standards of practice and competency measures.

A robust system of certification and registration of CE practitioners may be the solution as it would assist in the uptake of these quality improvement tools. Work towards certification and registration will be a project for the future.

Ottawa Heart Institute Research Corporation (OHIRC) Technology Management Program

2023-03-07T17:35:02-08:00

The absence of a structured technology management program for research equipment in the Ottawa Heart Institute Research Corporation (OHIRC) has been a deterrent leading to decreased uptime, unrepairable equipment, low collaboration between researchers, poor long-term capital planning, and increased costs. Apart from furniture, consumables, and fixtures, there is an active fleet of approximately 1700 pieces of equipment of which a few of them are being managed through service contracts. Therefore, a new technology management program for research equipment was developed, and currently oversees the inventory, documentation, scheduled and unscheduled maintenance, initial inspection, contract management, equipment procurement, decommission, and research grant application assistance. The designed workflows, processes, and tools are aligned with pre-existing processes in the Biomedical Engineering (BME) department supporting the clinical University of Ottawa Heart Institute (UOHI). A permanent, full-time BME Technologist dedicated to research equipment has been hired to fulfill long-term program requirements. The strategy has been slowly integrated, and it is currently under analysis to identify efficiencies and fulfill researchers' needs. Through the expertise of the Biomedical and Clinical Engineering departments, this work presents a novel strategy to manage research equipment within a healthcare facility.

Diagnostic Group for Elderly Patients With Loss of Autonomy – A feasibility Study

2023-03-07T14:57:07-08:00

A summary analysis of the workflow of the diagnostic services has shown that a large number of examinations concerning people over 65 years of age are possible in a safe manner in the patient's living environment. Knowing that the mobilization of elderly patients with loss of autonomy represents a challenge, in terms of logistics and on the health system itself, we have proposed certain diagnostic methods in geriatric centers which can be organized in Long-term hospital center (LTHC) or in the community. We believe that LTHCs could be associated with a diagnostic group for elderly person with loss of autonomy to capture this patient profile and thus reduce hospital traffic. This first approach is mainly philosophical. Then, with the collaboration of interested healthcare institutions, we will collect evidence from the past five years to better quantify the human, financial and reorganization needs. This reflection reinforces the need to develop support at home and in LTHCs for medical care that does not require intensive or critical medical monitoring.

Bridging the Gap for Real-Time Locating Functionality: Application of Medigate Data to Locate Devices

2023-03-07T17:10:26-08:00

With cybersecurity risk management becoming a priority for many health delivery organizations, a significant need for accuracy in medical device asset records has emerged. In order to address this gap, the application of automated networked asset discovery tools have become increasingly popular¹. These tools function to provide visibility on how devices communicate on the network; device-specific attributes such as MAC, IP, or software version can be automatically populated in the organization’s computerized maintenance management system (CMMS). Similarly, network connection details for these devices, such as system uptime or the last access point connected to, are also collected. As such, network connectivity details could be leveraged in physically locating devices. Dedicated real-time locating systems (RTLS) commonly serve this purpose in hospitals to locate patients or items of interest, frequently making use of existing hospital wireless network infrastructure^2,3. While highly accurate, a dedicated RTLS system requires the installation of a tag to track an object, deployment of a system server, and the allocation of funds to pay an annual subscription cost³. If a high level of accuracy is not needed, this case study serves to evaluate the viability of applying an existing automated networked asset discovery system to provide ‘low fidelity’ location data for the purposes of asset management.

Interior Health Authority (IHA) recently purchased an automated networked asset discovery tool known as Medigate, in response to a number of cybersecurity recommendations made by the BC Auditor General¹. This system has been integrated with Cisco DNA Spaces software used to map wireless access point (AP) information, allowing IHA Biomedical Engineering (BME) staff to locate wireless-enabled medical devices on a map, based on what AP they last connected to. After this integration had been implemented, several BME supervisors and technologists were granted access to the platform, with the intent of using the location data to find devices in need of maintenance or repair.

Feedback on the efficacy of this system was collected via a survey distributed to the BME supervisors and technologists given access to the platform. Initial feedback has been positive, with technologists appreciating how this tool reduces the amount of time spent trying to locate infusion pumps. This time savings was noted to be ‘extensive’ when multiple wireless devices were simultaneously due for maintenance. While generally positive, some limitations of the system were noted. If a device is turned off and then moved, the ‘last known location’ information will not be correct and could introduce a hurdle in locating the device. Additionally, due to the reliance on location data being provided by a single wireless access point, as opposed to triangulated data from a true RTLS system, device location data is accurate, but not precise.

Preliminary feedback on this Medigate integration has shown great potential thus far. While location data may not be precise, the tool is useful in narrowing down where a device may be, or if it may have travelled between sites. Furthermore, due to the reliance of this system on already-implemented infrastructure, the deployment of this system across all IHA sites was immediate; conventional RTLS systems are limited to the specific site in which they are deployed. Lastly, dedicated RTLS tags are visible within Medigate, so devices that are not wireless-capable (such as beds and stretchers), could potentially be tagged and located. While currently explorative, IHA foresees long-term value in improving maintenance interval compliance and reduction of ‘lost’ devices across the sites in our health region.

Current and Best Clinical Engineering Practices In The United States

2023-03-07T15:23:50-08:00

Clinical Engineering professionals in the United States (US) - Clinical Engineers and Biomedical Equipment Technicians (BMETs) play an important role in enhancing patient safety and managing the deployment and use of medical technology in healthcare organizations.

This presentation will share best US practices in managing different aspects of clinical engineering including inventory control, incoming inspections, planned maintenance, pre-purchase evaluation, managing equipment service contracts, hazard and recall notification, equipment installation, repairs and upgrade, purchase request review, equipment replacement planning, device incident review, and regulatory compliance.

The presentation will review the US clinical engineering education programs that educates and trains the clinical engineers and BMETs. The continuing education activities that help maintain and enhance these professionals so that they can effectively lead and execute complex medical technology projects and clinical engineering functions within the healthcare organization will be discussed.

The voluntary peer-developed US Certification program that demonstrates the competency and professional recognition of Clinical Engineers and BMETs by an externally validated examination will also be presented.

A proposal and justification for a parts designated position for the CHEO Clinical Engineering Regional Services

2023-03-07T15:14:08-08:00

Abstract (300 words)—Parts and the management of parts is
an age-old challenge for clinical engineering (CE) departments.
The Children’s Hospital of Eastern Ontario (CHEO) is at a
critical juncture whereby the demand for parts has increased
sufficiently such that it has limited the capacity of the
department to function and grow. Herein, the authors provide
the basis for a proposal and justification for a permanent
full-time parts designated position responsible for protecting
the value of the inventory on shelf. Its aim is to develop and
maintain a parts nomenclature system, analyze the dynamics of
the stocking levels and evaluate market analysis. This person
will also eliminate delays of providing a part for
repair/corrective maintenance, and write policies & standards
pertaining to parts management.

Implementing Cybersecurity Practices for Biomedical Engineering Departments

2023-03-07T15:18:35-08:00

An introduction to a step-by-step playbook for medical device security is analyzed through this paper. The research is on-going, however, this paper will introduce simple and cost effective practices that users of biomedical engineering departments can implement to help mitigate the potential threats to medical devices. Utilizing the information provided in this paper, biomedical engineering departments will begin their journey in evaluating the potential threats of cybersecurity attacks on medical devices and implementing risk-based security practices.

Provincial Structure of Clinical Engineering in Canada and its Management

2023-03-07T16:08:36-08:00

“Hospitals should have sufficient access to clinical engineering expertise.” [1] Clinical Engineering (CE) departments are a fundamental aspect of the smooth running of healthcare organizations since CE’s origin in the late 1960s [2]. From then, CE departments were tasked with various responsibilities, namely managing medical device technologies. While the role of a CE is similar across the world, each country differs in their funding structure, patient needs, and healthcare capabilities. Indeed, even the operation of healthcare within a country can change altogether between regions, affecting CE services which are directly influenced and controlled by administrative and corporate decisions [3]. In Canada, the standard for healthcare is set by the federal government but payment and management are set by the provincial government [4]. Therefore, CE structural changes happen often, depending on funding and public requirements. This paper will focus on Canada and investigate how the structure, management, and development of CE varies across the provinces and territories, and how the similarities and differences can lead to challenges and successes in CE.

A Success Story: Improving Documentation of Medical Device Patient Safety Events

2023-03-07T17:32:01-08:00

Improving documentation of medical device patient safety events in the computerized maintenance management system used by Lower Mainland Biomedical Engineering.

Infusion Pump Remediation in Lower Mainland B.C. Health Authorities

2023-03-07T16:54:17-08:00

Infusion pumps deliver fluids and medications at controlled flow or dose rates into a patient’s body. All health authorities within the Lower Mainland (LM), British Columbia are standardized to use Becton Dickinson (BD) Alaris Infusion pumps.

In 2020, BD initiated four voluntary recalls to address several software and hardware issues with the Alaris infusion system [1]-[4], all which have the potential to result in severe patient harm or death. In 2021, the required software and hardware fixes to mitigate these risks were approved by Health Canada. All device components of the Alaris infusion system require remediation, including PCU “Brains” and pumping modules. Every devices will be temporarily removed from clinical service to be upgraded by BD.

Within the LM, over 18,000 devices required remediation across 62 facilities, including 27 acute hospitals. Remediated devices are not backwards compatible with non-remediated devices (e.g., if nurse connects a remediated PCU with a non-remediated module an error occurs). Given the scope and incompatibility constraint, Lower Mainland Biomedical Engineering (LMBME) required a coordinated and planned approach to remediate all pumps.