Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 711 to 720 of 234,501 articles

A Multimodal Fusion Framework for Predicting the Life Cycle of Aerobic Granular Sludge: Integration of Fluorescence Spectra and Physicochemical Parameters.

Environmental research
Aerobic granular sludge (AGS) undergoes four stages throughout its life cycle, namely Adaptation, Growth, Maturation, and Disintegration. The dynamic evolution of these stages alters granule morphology and structural integrity, thereby affecting wast... read more 

Molecular insights into microbial transformation of dissolved organic matter from a wastewater treatment plant.

Environmental research
Dissolved organic matter (DOM) in treated wastewater influences effluent reactivity and ecological risks. How biological treatment configurations reshape DOM at the molecular level remains unclear. This study integrated molecular characterization, me... read more 

Automation in sleep state classification and maturity evaluation of EEG in preterm-born infants.

Neuroscience research
There is a growing need to automate EEG monitoring of preterm-born infants, particularly where expert evaluation is limited. However, existing models necessitate human intervention for comprehensive assessment, thereby limiting the clinical applicabi... read more 

Zoonotic Viral Spillover: From Reservoir Ecology and Viral Evolution to Pandemic Prevention.

Virus research
Zoonotic viral spillover represents a major global health challenge arising from complex interactions among viral evolution, reservoir ecology, host biology, environmental disruption, and human activities. Successful emergence requires viruses to ove... read more 

Residual risk in coronary artery disease: identification of cardio-reno-metabolic and psychosocial phenotypes in a Latin American Cohort of the INTERASPIRE registry.

Current problems in cardiology
BACKGROUND: Residual cardiovascular risk remains substantial in patients with established coronary artery disease despite contemporary secondary prevention strategies. Conventional approaches focused on isolated risk factors may fail to capture the m... read more 

Machine-learning versus traditional scoring methods for prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

Current problems in cardiology
BACKGROUND: Atrial fibrillation (AF) recurrence after catheter ablation remains common, with recurrence rates up to 45%. Traditional risk scores demonstrate only modest predictive performance. Machine learning (ML) approaches have shown promise in im... read more 

Achieving detailed medial temporal lobe segmentation with upsampled isotropic training from implicit neural representation.

NeuroImage
Imaging biomarkers in magnetic resonance imaging (MRI) are important tools for diagnosing, tracking and treating Alzheimer's disease (AD). Neurofibrillary tau pathology in AD is closely linked to neurodegeneration and generally follows a pattern of s... read more 

Machine learning integrating high-resolution T1-weighted MR radiomics of subcortical structures with blood biomarkers to identify depressed patients with a history of suicide attempts.

Progress in neuro-psychopharmacology & biological psychiatry
This study aimed to develop a machine learning (ML) model combining radiomic signatures from subcortical structures on T1-weighted MR images with peripheral blood biomarkers to distinguish depressed patients with versus without a history of suicide a... read more 

Rare Earth Element Biosensing in Chemically Complex Waters: Interference Mechanisms, Operating Windows, and Mitigation for a Lanmodulin Enzymatic Switch.

New biotechnology
Rare earth elements (REE) are increasingly mobilised from mine residues and other industrial sources, creating a need for rapid screening of chemically complex waters where conventional ICP-based analysis is impractical on site. Here, we evaluate a l... read more