Latest AI and machine learning research in military medicine for healthcare professionals.
Accurate pose estimation underpins quantitative analysis of behavior, yet many deep learning-based tracking tools remain optimized for offline workflows that rely on fragmented software pipelines, workstation-grade GPUs, or external middleware to enable real-time deployment. Here we present an integrated software-hardware ecosystem for pose estimation that spans dataset creation, model training, o...
Chronic wounds affect over 1.2 million Canadians and incur healthcare costs exceeding $13 billion annually, with global expenditures approaching $149 billion. Current clinical practice relies on manual measurements and subjective visual evaluations, which overestimate wound area by up to 40% and demonstrate poor-to-moderate inter-rater reliability. This variability complicates longitudinal monitor...
Abstract Purpose: Glaucoma, a leading cause of irreversible vision loss, often remains undiagnosed due to its asymptomatic progression and the limitat...
Image deblurring is a critical stage in mobile image signal processing pipelines, where the ability to restore fine structures and textures must be ba...
Sociotechnical challenges of machine learning in healthcare and social welfare are mismatches between how a machine learning tool functions and the st...
Deploying medical image segmentation models in routine clinical workflows is often constrained by on-premises infrastructure, where computational reso...
Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. ...
Vision-Language Models (VLMs) offer promising capabilities for mobile devices, but their deployment faces significant challenges due to computationa...
We investigate the feasibility of inferring emotional states exclusively from physiological signals, thereby presenting a privacy-preserving alterna...
Medical physics and clinical engineering (MPCE) professionals have a critical role in the safe and effective deployment of artificial intelligence (AI...
In recent years, hip arthroscopy has made great progress and has been extended to the treatment of intra-articular or periarticular diseases. However,...
The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data coll...
A Wi-Fi-enabled device, or simply Wi-Fi device, sporadically broadcasts probe request frames (PRFs) to discover nearby access points (APs), whether ...
The performance of leaning-based perception algorithms suffer when deployed in out-of-distribution and underrepresented environments. Outdoor robots...
Imitation learning models for robotic tasks typically rely on multi-modal inputs, such as RGB images, language, and proprioceptive states. While pro...
Magnetic Resonance Fingerprinting (MRF) is a fast quantitative MR Imaging technique that provides multi-parametric maps with a single acquisition. N...
The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine an...
The integration of artificial intelligence (AI) and machine learning-enabled medical technologies into clinical practice is expanding at an unpreceden...
Prolonged Exposure (PE) therapy is an effective treatment for post-traumatic stress disorder (PTSD), but evaluating therapist fidelity remains labor...
Safe deployment of machine learning (ML) models in safety-critical domains such as medical imaging requires detecting inputs with characteristics no...