Researchers successfully developed a machine learning-based method for predicting symptom deterioration in patients with cancer.

The CT-based whole-lung radiomic nomogram accurately identifies AECOPD and offers a robust tool for clinical diagnosis and treatment planning.

A tool combining CV risk score (CVRS) and coronary artery calcium score (CACS) facilitates stratification of patients with COPD at risk for MACE.

Objectives Disease activity assessment is important for Crohn’s disease (CD) management, since it involves the initial and subsequent therapeutic schedule. The purpose of this study is to identify a ...

Researchers sought to develop and validate artificial neural networks for overall survival and progression-free survival in older adults with HNSCC following definitive chemoradiation.

Obesity indices, particularly body fat percentage and relative fat mass, were associated with OA and may aid in early detection.

Simple information already gathered during routine well-baby visits could help clinicians spot which infants are at risk for persistent developmental delays, without the need for complex testing.

A machine learning model using basic clinical data can predict PH risk, identifying key predictors like low hemoglobin and elevated NT-proBNP. Researchers have developed a machine learning model that ...

A fully automated deep learning workflow for echocardiography in pulmonary arterial hypertension (PAH) is accurate and feasible.