RPS 2305 - Healthy aging, body composition and prevention: the true potential of AI?

Automated coronary calcification assessment on ungated unenhanced chest CT using an optimised nnUNet framework for patient prognostication in non-small-cell lung cancer

Deep learning-based biological age estimation from MRI predicts cardiometabolic events in the general population

Body Composition in the general population: MRI-derived reference curves from over 66,000 individuals and their association with cardiometabolic outcomes

AI-Driven MRI Biomarker Extraction and Machine Learning Analysis of Their Association with Diabetes: A UK Biobank Study

CompositIA: an open-source pipeline for automated quantification of body composition scores from thoraco-abdominal CT scans

A novel CT-based biological age model, based on automated abdominal CT biomarkers for accurate longevity prediction

Enabling Exchange of Quantitative CT-Assessed Body Composition Data using FHIR: A First Step into Interoperable Body Composition Profiling

Transfer of a CT-based 3D body composition analysis segmentation model to MRI T2-weighted sequences using a generative adversarial network

Deep Learning-Based Fully Automated Body Composition Analysis as a Prognostic Factor in ARDS Patients using CT-Based Opportunistic Biomarkers

Enhancing Autopsy Evaluations with AI-Driven Body Composition Biomarkers from Post-Mortem CT Scans

Deep Learning Models for Cardiomegaly Detection Enables Assessment of Cardiomegaly Prevalence in an International CT Data Repository: Insights from AICT Consortium

Detection of osteoporotic vertebral body compression fracture in computed tomography scans of the chest and abdomen using artificial intelligence Nanox.AI