AI Medical Compendium

Dynamont: A comprehensive cross-species comparison of ONT segmentation tools.

GigaScience Jan 19, 2026

BACKGROUND: Oxford Nanopore Technologies (Oxford Nanopore Technologies (ONT)) sequencing enables direct, long-read sequencing of DNA and RNA, preserving nucleotide modifications. During basecalling, deep neural networks translate raw nanopore signals... read more

Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen healthcare settings.

GigaScience Jan 19, 2026

BACKGROUND: The deployment of machine learning in clinical settings is often hindered by the limited generalizability of the models. Models that perform well during development tend to underperform in new environments, limiting their clinical utility... read more

Spectral entropy variability of intraoperative electrocorticography predicts outcome after epilepsy surgery in people with focal cortical dysplasia.

Epilepsia Jan 19, 2026

OBJECTIVE: Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is difficult to predict. We explored whether spectral entropy, a fast computable electroencephalographic (EEG... read more

Fibroblast-like cells accumulate late in human coronary atherosclerosis contributing to necrotic core formation.

Cardiovascular research Jan 19, 2026

AIMS: Proliferation of arterial smooth muscle cells (SMCs) and their modulation to alternative mesenchymal phenotypes is central to atherosclerotic lesion growth. It has been studied extensively in mouse models, but a detailed analysis of when and wh... read more

Using Machine Learning to Predict First-Order Reaction Rate Constants of PFAS Degradation.

Bulletin of environmental contamination and toxicology Jan 19, 2026

Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent pollutants, posing challenges for effective remediation. This study presented a machine learning (ML) framework to predict the first-order reaction rate constant (k) of PFAS de... read more

Machine Learning Water Pollutants, Chemical Models, Chemical Environmental Pollutants Kinetics Fluorocarbons

Artificial intelligence analysis of minimally invasive surgery data.

Journal of robotic surgery Jan 19, 2026

Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology Jan 19, 2026

BACKGROUND: Sarcopenia, characterized by progressive skeletal muscle loss, is associated with poor outcomes in various diseases. Traditional methods for assessing muscle cross-sectional area using computed tomography (CT) scans are manual, time-consu... read more

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

Pediatric radiology Jan 19, 2026

BACKGROUND: Radiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-learning reconstruction may enable ultra-low-dose imaging comparable to photon-counting CT. OBJECTIVE: ... read more

Preoperative CT imaging and machine learning models for predicting ureteral access sheath placement success in non-stented patients with ureteral calculi: a retrospective cohort study.

World journal of urology Jan 19, 2026

OBJECTIVE: This study aims to both develop and evaluate a predictive model for ureteral access sheath(UAS)placement success using preoperative CT-based 3D ureteral imaging and machine learning techniques. Specifically, it investigates the impact of u... read more

Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Aged Adult Male Middle Aged Cohort Studies Imaging, Three-Dimensional Treatment Outcome Predictive Value of Tests Preoperative Care Ureter Ureteroscopy Ureteral Calculi Lithotripsy

Systematic review and meta-analysis of AI accuracy in warfarin dose prediction across ethnic groups.

European journal of clinical pharmacology Jan 19, 2026

PURPOSE: The primary purpose of this study is to systematically evaluate how accurately artificial intelligence (AI) models can predict optimal warfarin dosing by incorporating both genetic variations-particularly in VKORC1 and CYP2C9-and clinical pa... read more

Humans Artificial Intelligence Anticoagulants Warfarin Ethnicity Cytochrome P-450 CYP2C9 Vitamin K Epoxide Reductases

Artificial Intelligence Medical Compendium