Artificial Intelligence Medical Compendium

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

Showing 28,391 to 28,400 of 219,260 articles

Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy.

Clinical neuroradiology
PURPOSE: Metachromatic leukodystrophy (MLD) is a rare lysosomal storage disorder characterized by progressive white matter demyelination. Quantification of demyelinated white matter on MRI-typically expressed as the demyelination load-serves as a key... read more 

Beta cell microRNAs function as molecular hubs of type 1 diabetes pathogenesis and as biomarkers of diabetes risk.

Diabetologia
AIMS/HYPOTHESIS: Clinically actionable biomarkers that accurately reflect the health status of the beta cell are needed to improve risk stratification and optimise the timing of interventions in type 1 diabetes. We hypothesised that inflammatory stre... read more 

Harnessing machine learning and multi-scale modeling to discover novel ALOX15 inhibitors from marine natural products.

Molecular diversity
ALOX15 is a key regulatory enzyme in multiple pathological processes including inflammation, cancer, and cardiovascular disease, rendering the development of potent inhibitors of this enzyme of significant clinical importance. This study aims to scre... read more 

Deep learning-based early prediction of carotid plaque response to lipid-lowering therapy using longitudinal multimodal ultrasound imaging.

Insights into imaging
OBJECTIVE: This study aimed to develop and validate a deep learning prediction model using longitudinal multimodal ultrasound imaging for early identification of treatment-sensitive and treatment-resistant carotid plaques in patients receiving lipid-... read more 

Artificial intelligence-based screening of phytochemicals for targeted cancer therapy.

Natural products and bioprospecting
Cancer remains one of the leading causes of death worldwide and continues to pose a serious public health challenge. The limited success of many current treatments-often due to toxicity, poor selectivity, and the development of drug resistance-highli... read more 

Interpretable machine learning models based on CMR radiomics for predicting left ventricular diastolic dysfunction in patients with metabolic-associated steatotic liver disease and type 2 diabetes mellitus.

Journal of diabetes investigation
BACKGROUND: Obesity-induced left ventricular diastolic dysfunction (LVDD), associated with ectopic fat and dysfunctional epicardial adipose tissue (EAT), is emerging as a key research area due to its increasing prevalence and links to metabolic-assoc... read more