Artificial Intelligence Medical Compendium

BACKGROUND: Cervical nodal metastasis remains one of the strongest determinants of treatment planning and prognosis in oral squamous cell carcinoma. Histopathology remains the reference standard, but it does not provide immediate information at the p... read more 

OBJECTIVE: Colorectal cancer (CRC) is molecularly, cellularly and spatially heterogeneous, and current staging systems and biomarkers incompletely explain prognosis and treatment response. This study integrated transcriptomics, machine learning, sing... read more 

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BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an important cause of heart failure (HF) with increased left ventricular (LV) wall thickness that can be challenging to distinguish from other LV hypertrophic processes. Echocardiographic ... read more 

BACKGROUND: Cardiogenic shock (CS) exhibits marked clinical heterogeneity, limiting the performance of conventional risk stratification tools like the Society for Cardiovascular Angiography and Interventions (SCAI) staging system. Machine learning (M... read more 

BACKGROUND: Automated echocardiography analysis using artificial intelligence (AI) currently lacks effective guardrails or oversight mechanisms to ensure its safe and reliable deployment. OBJECTIVES: This study investigated whether deep feature repre... read more 

BACKGROUND: Early detection of gastric cancer (GC) is limited by invasive endoscopy and poor sensitivity of blood-based biomarkers. Red blood cells (RBCs), the most abundant cells in the circulation, are anucleate but can retain DNA remnants that hav... read more 

Recent developments in wearable devices have opened the possibility of heart rate variability (HRV), a quantitative measure of autonomic nervous system (ANS) regulation, as a potential indicator for the early detection of psychiatric disorders. This ... read more 

In microbial systems, many biochemical functions arise from pathways encoded and executed at the community level. The collective nature of these functions complicates bottom-up efforts to determine each species' contribution. Previous work has shown ... read more 

Automated electrocardiogram (ECG) classifiers require uncertainty procedures that address class-specific clinical risk in intrinsically multi-label data. Approach: We propose per-class risk-controlling prediction sets for multi-label ECG classifi... read more