FocusedON-BC: A Robust Deep Learning Framework for Automated Body Composition Assessment

Background: Computed tomography-based body composition assessment enables the quantification of clinically relevant prognostic conditions such as sarcopenia, myosteatosis, and visceral adiposity; the manual segmentation process limits its routine implementation in clinical practice. We developed FocusedON-BC, an automated deep learning tool for opportunistic screening of skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) across the T12–L5 range; Methods: Validated on a multicenter cohort of 518 patients (3280 slices) with diverse body mass index (12.7–47.7 kg/m2) from different computed tomography manufacturers. Performance was benchmarked against expert segmentation using the Dice coefficient score (DSC) and the mean absolute error (MAE); Results: FocusedON-BC achieved expert-level accuracy: mean DSC was 0.974±0.010 (SM), 0.959±0.032 (VAT), and 0.986±0.014 (SAT). Clinical MAE remained