Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection

Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and evaluations for treatment. DFU patient population is on the rise and will soon outpace the available health resources. Autonomous monitoring and evaluation of DFU wounds is a much-needed area in health care. In this paper, we evaluate and identify the most accurate feature extractor that is the core basis for developing a deep-learning wound detection network. For the evaluation, we used mAP and F1-score on the publicly available DFU2020 dataset. A combination of UNet and EfficientNetb3 feature extractor resulted in the best evaluation among the 14 networks compared. UNet and Efficientnetb3 can be used as the classifier in the development of a comprehensive DFU domain-specific autonomous wound detection pipeline.Comment: 5 pages, 2 figures, 3 tables, 2022 IEEE International Conference on Data Mining Workshop

Hand contour detection in wearable camera video using an adaptive histogram region of interest

BACKGROUND: Monitoring hand function at home is needed to better evaluate the effectiveness of rehabilitation interventions. Our objective is to develop wearable computer vision systems for hand function monitoring. The specific aim of this study is to develop an algorithm that can identify hand contours in video from a wearable camera that records the user’s point of view, without the need for markers. METHODS: The two-step image processing approach for each frame consists of: (1) Detecting a hand in the image, and choosing one seed point that lies within the hand. This step is based on a priori models of skin colour. (2) Identifying the contour of the region containing the seed point. This is accomplished by adaptively determining, for each frame, the region within a colour histogram that corresponds to hand colours, and backprojecting the image using the reduced histogram. RESULTS: In four test videos relevant to activities of daily living, the hand detector classification accuracy was 88.3%. The contour detection results were compared to manually traced contours in 97 test frames, and the median F-score was 0.86. CONCLUSION: This algorithm will form the basis for a wearable computer-vision system that can monitor and log the interactions of the hand with its environment

Kinetics Analysis Of Multi-Segment Trunk After Experimental Errors Minimization

A two-dimensional analytical heat conduction model of an annular composite fin has been carried out. The composite fins composed of a porous polyethylene core, a square aluminum insert, and metallic zinc coating layers, was fabricated using wire-arc spraying technology. Analytical solutions of temperature distribution, energy dissipation and fin efficiency through the fins at natural convection condition have been proposed

Synthesizing Diabetic Foot Ulcer Images with Diffusion Model

Diabetic Foot Ulcer (DFU) is a serious skin wound requiring specialized care. However, real DFU datasets are limited, hindering clinical training and research activities. In recent years, generative adversarial networks and diffusion models have emerged as powerful tools for generating synthetic images with remarkable realism and diversity in many applications. This paper explores the potential of diffusion models for synthesizing DFU images and evaluates their authenticity through expert clinician assessments. Additionally, evaluation metrics such as Frechet Inception Distance (FID) and Kernel Inception Distance (KID) are examined to assess the quality of the synthetic DFU images. A dataset of 2,000 DFU images is used for training the diffusion model, and the synthetic images are generated by applying diffusion processes. The results indicate that the diffusion model successfully synthesizes visually indistinguishable DFU images. 70% of the time, clinicians marked synthetic DFU images as real DFUs. However, clinicians demonstrate higher unanimous confidence in rating real images than synthetic ones. The study also reveals that FID and KID metrics do not significantly align with clinicians' assessments, suggesting alternative evaluation approaches are needed. The findings highlight the potential of diffusion models for generating synthetic DFU images and their impact on medical training programs and research in wound detection and classification.Comment: 8 pages, 3 figures, 6th Workshop on AI for Aging, Rehabilitation and Intelligent Assisted Living at European Conference on Machine Learning, Italy, 202

Closed-Loop Interruption of Hippocampal Ripples through Fornix Stimulation in the Non-Human Primate

AbstractBackgroundHippocampal sharp-wave ripples (SWRs) arising from synchronous bursting in CA3 pyramidal cells and propagating to CA1 are thought to facilitate memory consolidation. Stimulation of the CA3 axon collaterals comprising the hippocampal commissure in rats interrupts sharp-wave ripples and leads to memory impairment. In primates, however, these commissural collaterals are limited. Other hippocampal fiber pathways, like the fornix, may be potential targets for modulating ongoing hippocampal activity, with the short latencies necessary to interrupt ripples.ObjectiveThe aim of this study is to determine the efficacy of closed-loop stimulation adjacent to the fornix for interrupting hippocampal ripples.MethodStimulating electrodes were implanted bilaterally alongside the fornix in the macaque, together with microelectrodes targeting the hippocampus for recording SWRs. We first verified that fornix stimulation reliably and selectively evoked a response in the hippocampus. We then implemented online detection and stimulation as hippocampal ripples occurred.ResultsThe closed-loop interruption method was effective in interrupting ripples as well as the associated hippocampal multi-unit activity, demonstrating the feasibility of ripple interruption using fornix stimulation in primates.ConclusionAnalogous to murine research, such an approach will likely be useful in understanding the role of SWRs in memory formation in macaques and other primates sharing these pathways, such as humans. More generally, closed-loop stimulation of the fornix may prove effective in interrogating hippocampal-dependent memory processes. Finally, this rapid, contingent-DBS approach may be a means for modifying pathological high-frequency events within the hippocampus, and potentially throughout the extended hippocampal circuit

Model-based closed-loop control of thalamic deep brain stimulation

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients’ symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity.Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim–DBS are linked to symptomatic changes in EMG signals. By using a proportional–integral–derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim–DBS so that the power of EMG reaches a desired control target.Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems

Multi-center, single-blind randomized controlled trial comparing functional electrical stimulation therapy to conventional therapy in incomplete tetraplegia

BackgroundLoss of upper extremity function after tetraplegia results in significant disability. Emerging evidence from pilot studies suggests that functional electrical stimulation (FES) therapy may enhance recovery of upper extremity function after tetraplegia. The aim of this trial was to determine the effectiveness of FES therapy delivered by the Myndmove stimulator in people with tetraplegia.MethodsA multi-center, single-blind, parallel-group, two-arm, randomized controlled trial was conducted comparing FES to conventional therapy in adults (≥18 years) with C4–C7 traumatic incomplete tetraplegia between 4 and 96 months post-injury, and with a baseline spinal cord injury independence measure III -self-care (SCIM III-SC) score of ≤10. Participants were enrolled at four SCI-specialized neurorehabilitation centers in the U.S. and Canada. Participants were stratified by center and randomized in a 1:1 ratio to receive either 40 sessions of FES or conventional therapy targeting upper extremities over a 14-week period. Blinded assessors measured SCIM III, Toronto Rehabilitation Institute Hand Function Test, and Graded Redefined Assessment of Strength, Sensibility, and Prehension at baseline, after 20th session, after 40th session or 14 weeks after 1st session, and at 24 weeks after 1st session. The primary outcome measure was change in SCIM III-SC from baseline to end of the treatment. Based on the primary outcome measure, a sample size of 60 was calculated. Seventeen participants' progress in the study was interrupted due to the COVID-19 lockdown. The protocol was modified for these participants to allow them to complete the study.ResultsBetween June 2019 to August 2021, 51 participants were randomized to FES (n = 27) and conventional therapy (n = 24). Both groups gained a mean of 2 points in SCIM-SC scores at the end of treatment, which was a clinically meaningful change. However, there was no statistically significant difference between the groups on any outcomes.ConclusionForty sessions of FES therapy delivered by the MyndMove stimulator are as effective as conventional therapy in producing meaningful functional improvements that persist after therapy is completed. Limitations of this study include the impact of COVID-19 limiting the ability to recruit the target sample size and per-protocol execution of the study in one-third of the participants.RegistrationThis trial is registered at www.ClinicalTrials.gov, NCT03439319

Editorial: Wearable and Implantable Technologies in the Rehabilitation of Patients With Sensory Impairments

Editorial on the Research Topic Wearable and Implantable Technologies in the Rehabilitation of Patients With Sensory Impairment

The graded redefined assessment of strength sensibility and prehension: reliability and validity.

Abstract With the advent of new interventions targeted at both acute and chronic spinal cord injury (SCI), it is critical that techniques and protocols are developed that reliably evaluate changes in upper limb impairment/function. The Graded Redefined Assessment of Strength Sensibility and Prehension (GRASSP) protocol, which includes five subtests, is a quantitative clinical upper limb impairment measure designed for use in acute and chronic cervical SCI. The objectives of this study were to: (1) establish the inter-rater and test-retest reliability, and (2) establish the construct and concurrent validity with the International Standards of Neurological Classification of Spinal Cord Injury (ISNCSCI), Spinal Cord Independence Measure II (SCIM), and the Capabilities of Upper Extremity Questionnaire (CUE). The study protocol included repeated administration of the GRASSP to a cross-section of individuals with tetraplegia who were neurologically stable (n=72). ISNCSCI, CUE, and SCIM assessments were also administered. Two assessors examined the individuals over a 7-day period. Reliability was tested with intra-class correlation coefficients; construct validity was established with agreement/discordance analysis between the GRASSP and ISNCSCI sensory and motor items; and concurrent validity was tested with Spearman correlation coefficients. Inter-rater and test-retest reliability for all subtests within the GRASSP were above the hypothesized value of 0.80 (0.84-0.96 and 0.86-0.98, respectively). The GRASSP is about 50% more sensitive (construct validity) than the ISNCSCI when defining sensory and motor integrity of the upper limb; the subtests showed concurrence with the SCIM, SCIM self-care subscale, and CUE. The strongest concurrence to impairment was with self-perception of function (CUE) (0.57-0.83, p\u3c0.0001). The GRASSP was found to demonstrate reliability, construct validity, and concurrent validity for use as a standardized upper limb impairment measure for individuals with tetraplegia