Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population.

ABSTRACT: Aging is an important risk factor for disease, leading to morphological change that can be assessed on Computed Tomography (CT) scans. We propose a deep learning model for automated age estimation based on CT- scans of the thorax and abdomen generated in a clinical routine setting. These predictions could serve as imaging biomarkers to estimate a "biological" age, that better reflects a patient's true physical condition. A pre-trained ResNet-18 model was modified to predict chronological age as well as to quantify its aleatoric uncertainty. The model was trained using 1653 non-pathological CT-scans of the thorax and abdomen of subjects aged between 20 and 85 years in a 5-fold cross-validation scheme. Generalization performance as well as robustness and reliability was assessed on a publicl

SUBMITTER: Kerber B 

PROVIDER: S-EPMC10629654 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature