A5074800190- Ultrasound in Clinical Applications
- COVID-19 diagnosis using AI
- Phonocardiography and Auscultation Techniques
- Human Mobility and Location-Based Analysis
- Data-Driven Disease Surveillance
- Myofascial pain diagnosis and treatment
- Complex Network Analysis Techniques
- Traffic Prediction and Management Techniques
- Musculoskeletal pain and rehabilitation
- Radiology practices and education
- Shoulder Injury and Treatment
Novateur Research Solutions (United States)
2024
Johns Hopkins University
2022-2024
Abstract Background Deep neural networks (DNNs) to detect COVID-19 features in lung ultrasound B-mode images have primarily relied on either vivo or simulated as training data. However, suffer from limited access required manual labeling of thousands image examples, and can poor generalizability due domain differences. We address these limitations identify the best strategy. Methods investigated feature detection with DNNs trained our carefully datasets (40,000 images), publicly available...
Abstract Background Dysfunctional gliding of deep fascia and muscle layers forms the basis myofascial pain dysfunction, which can cause chronic shoulder pain. Ultrasound shear strain imaging may offer a non-invasive tool to quantitatively evaluate extent muscular dysfunctional its correlation with This case study is first use ultrasound report between pectoralis major minor muscles in shoulders without Case presentation The during rotation volunteer was measured imaging. results show that...
Collecting real-world mobility data is challenging. It often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomalies in large-scale nearly impossible, as it demands meticulous effort to distinguish subtle complex patterns. These challenges significantly impede progress geospatial anomaly detection research by restricting access reliable complicating the rigorous evaluation, comparison, benchmarking of methodologies. To address...
Deep learning has been implemented to detect COVID-19 features in lung ultrasound B-mode images. However, previous work primarily relied on vivo images as the training data, which suffers from limited access required manual labeling of thousands image examples. To avoid this labeling, is tedious and time consuming, we propose detection (i.e., A-line, B-line, consolidation) with deep neural networks (DNNs) trained simulated The simulation-trained DNNs were tested healthy subjects patients....
COVID-19 is a highly infectious disease with high morbidity and mortality, requiring tools to support rapid triage risk stratification. In response, deep learning has demonstrated great potential quicklyand autonomously detect features in lung ultrasound B-mode images. However, no previous work considers the application of these models signal processing stages that occur prior traditional image formation. Considering multiple required achieve images, our research objective investigate most...
Existing methods for anomaly detection often fall short due to their inability handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integrates Bayesian principles with deep neural networks model underlying multivariate distributions from sparse complex datasets. Unlike traditional models, DeepBayesic is designed manage heterogeneous inputs, accommodating both continuous categorical...
Existing methods for anomaly detection often fall short due to their inability handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integrates Bayesian principles with deep neural networks model underlying multivariate distributions from sparse complex datasets. Unlike traditional models, DeepBayesic is designed manage heterogeneous inputs, accommodating both continuous categorical...