Latest AI and machine learning research in peripheral artery disease for healthcare professionals.
Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. Liver function biomarkers (LFBs), particularly gamma - glutamyl transferase (GGT), have been linked to cardiovascular outcomes, yet their contribution to hard ASCVD risk prediction is not well defined. Methods: This study analyzed data from the Natio...
Target trial emulation (TTE) enables causal inference from observational data but remains bottlenecked by manual, expert-dependent protocol operationalization. While large language models (LLMs) have advanced clinical knowledge extraction and code generation, their ability to automate end-to-end TTE workflows remains largely unexplored. We present an LLM-driven framework using retrieval-augmented ...
Significant advancements made in reconstructing hands from images have delivered accurate single-frame estimates, yet they often lack physics consiste...
Background: Pretest probability (PTP) models using clinical risk factors guide decision-making for coronary artery disease (CAD). Existing models (Upd...
Atrial fibrillation and heart failure impose substantial health burdens worldwide, yet existing prediction models lack sufficient accuracy and general...
Iris presentation attack detection (PAD) is critical for secure biometric deployments, yet developing specialized models faces significant practical b...
Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep lear...
Koopman operator theory provides a global linear representation of nonlinear dynamics and underpins many data-driven methods. In practice, however, fi...
Background. Atherosclerosis is increasingly recognized as a chronic immunometabolic disorder involving complex interactions between circulating immune...
Clinical diagnosis of skin lesions integrates visual dermoscopic features with patient context such as age, skin type, and lesion characteristics. How...
The identification of genetic perturbations that can reverse disease-associated cellular phenotypes toward a healthy state is a central challenge in e...
We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized...
How habitual diet influences the gut microbiome and plasma metabolome across insulin resistance states remains unclear. We conducted year-long multi-o...
Aims: Despite the availability of clinical risk scores for atherosclerotic cardiovascular disease (ASCVD), their use is limited because the required p...
AI models for medical diagnosis often exhibit uneven performance across patient populations due to heterogeneity in disease prevalence, imaging appear...
The applications of fingertip haptic devices have spread to various fields from revolutionizing virtual reality and medical training simulations to ...
The analysis of carotid arteries, particularly plaques, in multi-sequence Magnetic Resonance Imaging (MRI) data is crucial for assessing the risk of...
In this work, we study the problem pertaining to personalized classification of subclinical atherosclerosis by developing a hierarchical graph neura...
Purpose: The primary aim of this study is to enhance fault diagnosis in induction machines by leveraging the Pad\'e Approximant Neuron (PAON) model....
Ramadan fasting is a sacred ritual observed by approximately 1.8 billion Muslims each year, most of whom adhere to fasting due to its significance as ...