Evaluation of Surgical Skill Using Machine Learning with Optimal Wearable Sensor Locations

Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation

Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms

Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries

Common coding variant in SERPINA1 increases the risk for large artery stroke

Large artery atherosclerotic stroke (LAS) shows substantial heritability not explained by previous genome-wide association studies. Here, we explore the role of coding variation in LAS by analyzing variants on the HumanExome BeadChip in a total of 3,127 cases and 9,778 controls from Europe, Australia, and South Asia. We report on a nonsynonymous single-nucleotide variant in serpin family A member 1 (SERPINA1) encoding alpha-1 antitrypsin [AAT; p.V213A; P = 5.99E-9, odds ratio (OR) = 1.22] and confirm histone deacetylase 9 (HDAC9) as a major risk gene for LAS with an association in the 3?-UTR (rs2023938; P = 7.76E-7, OR = 1.28). Using quantitative microscale thermophoresis, we show that M1 (A213) exhibits an almost twofold lower dissociation constant with its primary target human neutrophil elastase (NE) in lipoprotein-containing plasma, but not in lipid-free plasma. Hydrogen/deuterium exchange combined with mass spectrometry further revealed a significant difference in the global flexibility of the two variants. The observed stronger interaction with lipoproteins in plasma and reduced global flexibility of the Val-213 variant most likely improve its local availability and reduce the extent of proteolytic inactivation by other proteases in atherosclerotic plaques. Our results indicate that the interplay between AAT, NE, and lipoprotein particles is modulated by the gate region around position 213 in AAT, far away from the unaltered reactive center loop (357-360). Collectively, our findings point to a functionally relevant balance between lipoproteins, proteases, and AAT in atherosclerosis

DeepTracking from Aerial Platforms

Object tracking is a pivotal part of video processing. Defense based UAVs generally fly at an altitude of 20,000-30,000ft. Conventionally, it's quite easy to track objects from a stationary camera's feed. Videos from UAVs are hard to process beca1use of abrupt motion of the UAV, jitters, noise and cluttering produced in the video. Tracking and surveillance from such altitudes is quite challenging. This paper illustrates the combination of mod- ified YOLOv4 algorithm with Darknet framework to perform object detection and the DeepSORT tracker to perform object tracking in such challenging environments. The detection model achieves an 11% improvement in Mean Average Precision(mAP) when compared with its predecessors on custom images. When paired with DeepSORT and inferred on a high-end GPU it renders output at 28 FPS, making it suitable for real-time tracking. It is also immune to occlusion and camera movement. The proposed model addresses problems in the existing models that require manual locking of target and high computational complexity. In addition to this, it aims to illustrate automatic tracking of moving objects in real-time. © 2021 IEEE

The inextricable axis of targeted diagnostic imaging and therapy: An immunological natural history approach

In considering the challenges of approaches to clinical imaging, we are faced with choices that sometimes are impacted by rather dogmatic notions about what is a better or worse technology to achieve the most useful diagnostic image for the patient. For example, is PET or SPECT most useful in imaging any particular disease dissemination? The dictatorial approach would be to choose PET, all other matters being equal. But is such a totalitarian attitude toward imaging selection still valid? In the face of new receptor targeted SPECT agents one must consider the remarkable specificity and sensitivity of these agents. (99m)Tc-Tilmanocept is one of the newest of these agents, now approved for guiding sentinel node biopsy (SLNB) in several solid tumors. Tilmanocept has a Kd of 3×10(-11)M, and it specificity for the CD206 receptor is unlike any other agent to date. This coupled with a number of facts, that specific disease-associated macrophages express this receptor (100 to 150 thousand receptors), that the receptor has multiple binding sites for tilmanocept (>2 sites per receptor) and that these receptors are recycled every 15 min to bind more tilmanocept (acting as intracellular "drug compilers" of tilmanocept into non-degraded vesicles), gives serious pause as to how we select our approaches to diagnostic imaging. Clinically, the size of SLNs varies greatly, some, anatomically, below the machine resolution of SPECT. Yet, with tilmanocept targeting, the SLNs are highly visible with macrophages stably accruing adequate (99m)Tc-tilmanocept counting statistics, as high target-to-background ratios can compensate for spatial resolution blurring. Importantly, it may be targeted imaging agents per se, again such as tilmanocept, which may significantly shrink any perceived chasm between the imaging technologies and anchor the diagnostic considerations in the targeting and specificity of the agent rather than any lingering dogma about the hardware as the basis for imaging approaches. Beyond the elements of imaging applications of these agents is their evolution to therapeutic agents as well, and even in the neo-logical realm of theranostics. Characteristics of agents such as tilmanocept that exploit the natural history of diseases with remarkably high specificity are the expectations for the future of patient- and disease-centered diagnosis and therapy