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

• Published in: PloS one Vol. 18; no. 11; p. e0292993
• Main Authors: Kerber, Bjarne, Hepp, Tobias, Küstner, Thomas, Gatidis, Sergios
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 07.11.2023; Public Library of Science (PLoS)
• Subjects: Abdomen; Age; Age determination; Aging; Analysis; Aorta; Automation; Biology and Life Sciences; Biomarkers; Chronology; Computed tomography; Computer and Information Sciences; CT imaging; Datasets; Decision-making; Deep learning; Emergency medical care; Estimation accuracy; Evaluation; Females; Health aspects; Machine learning; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Medicine and Health Sciences; Patients; Reliability analysis; Research and Analysis Methods; Risk factors; Salience; Science Policy; Spine; Spine (lumbar); Thorax; Tomography; Uncertainty; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0292993

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 publicly available test dataset consisting of thorax-abdomen CT-scans of 421 subjects. Score-CAM saliency maps were generated for interpretation of model outputs. We achieved a mean absolute error of 5.76 ± 5.17 years with a mean uncertainty of 5.01 ± 1.44 years after 5-fold cross-validation. A mean absolute error of 6.50 ± 5.17 years with a mean uncertainty of 6.39 ± 1.46 years was obtained on the test dataset. CT-based age estimation accuracy was largely uniform across all age groups and between male and female subjects. The generated saliency maps highlighted especially the lumbar spine and abdominal aorta. This study demonstrates, that accurate and generalizable deep learning-based automated age estimation is feasible using clinical CT image data. The trained model proved to be robust and reliable. Methods of uncertainty estimation and saliency analysis improved the interpretability.