Artificial Intelligence Medical Compendium

Falls among older adults are a major health concern, often resulting in severe injuries and diminished quality of life. Early prediction of fall risk and timely intervention can significantly reduce occurrence of negative health outcomes. To date, da... read more 

Anomaly detection is essential in various domains, where identifying rare and irregular samples is critical for system safety and security. This problem encounters a significant challenge due to extreme class imbalance, where normal samples vastly ou... read more 

Visual reinforcement learning has proven effective in addressing control tasks with high-dimensional image observations. However, obtaining generalizable representations for policy learning under various visual interferences remains a significant cha... read more 

High-quality fundus images provide essential anatomical information for clinical screening and ophthalmic disease diagnosis. Yet, due to hardware limitations, operational variability, and patient compliance, fundus images often suffer from low resolu... read more 

The matrix-based Rényi's α-order entropy, a powerful tool in information-theoretical learning, offers a direct computation from empirical data without requiring knowledge of the underlying probability density function (PDF). However, its exact comput... read more 

Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy, posing risks to both maternal and neonatal health. Artificial intelligence (AI) and machine learning (ML)-based solutions hold the promise of improving GDM predic... read more 

Classifying cancer subtypes is crucial for clinical diagnosis and therapy. Recently, graph neural network (GNN)-based methods have been explored for this task, but they often rely on predefined, explicit graph structures. In practice, such prior stru... read more 

Osteoarthritis (OA) is a degenerative joint disease which lacks reliable biomarkers for monitoring disease progression. Current assessment methods rely primarily on radiographic Kellgren-Lawrence (K-L) grading and symptom-based scores, which often sh... read more 

Microfluidic systems hold transformative potential for biosensing and diagnostic applications, yet conventional platforms remain constrained by complex fabrication processes, passive fluid control, and limited automation capabilities. To address thes... read more 

Dynamic oxygen sensing technology facilitates the early diagnosis of diseases, the optimization of treatment, and the enhancement of athletic performance by monitoring oxygen levels in human tissues and bodily fluids. While traditional methods, such ... read more