Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs

2026-07-20

AI Summary

This study proposes the first multi-task deep learning ensemble model for jointly estimating adult sex, age, height, and weight from digitally reconstructed radiographs (DRRs) generated in the coronal plane from CT scans. The approach fine-tunes three backbone architectures—ConvNeXt-Base, ViT-Base/16, and MaxViT-Base—and combines their predictions via weighted averaging, trained and validated on an institution-level split dataset. On the test set, the model achieves a sex classification accuracy of 0.997 and mean absolute errors of 3.57 years, 2.59 cm, and 3.40 kg for age, height, and weight, respectively. Performance improves further when using chest-to-pelvis scans, and the model successfully recapitulates expected trends in organ volume variation with physiological parameters, demonstrating robust generalization across diverse populations.

Abstract

Purpose: To develop and validate a deep learning ensemble for estimating adult sex, age, height, and weight from coronal digitally reconstructed radiographs (DRRs) generated from diagnostic CT. Materials and Methods: This retrospective study included 128,621 CT examinations from 80,004 adults at nine institutions in Japan. Three multitask models-ConvNeXt-Base, ViT-Base/16, and MaxViT-Base-were fine-tuned using coronal DRRs and combined by weighted averaging. Data were split by institution into training (114,147 examinations; seven institutions), tuning (4,305; one institution), and test (10,169; one institution) sets; generalizability was assessed on two non-Japanese datasets. Accuracy and mean absolute error (MAE) were used to evaluate sex classification and age, height, and weight regression, respectively. Body surface area (BSA)-corrected heart and liver volume trends were compared using true versus estimated height and weight. Results: In the test set (median age, 69.9 years; 4,899 of 10,169 [48.2%] male), overall sex-classification accuracy was 0.997 (95% CI, 0.996-0.998), and MAEs were 3.57 years (3.51-3.63), 2.59 cm (2.54-2.64), and 3.40 kg (3.34-3.47) for age, height, and weight, respectively. In examinations covering the chest through pelvis, accuracy was 1.000, and MAEs were 3.15 years, 2.28 cm, and 3.18 kg, respectively. BSA calculated from estimated values reproduced age-related heart and liver volume trends obtained using true values. On non-Japanese datasets, height error increased but was reduced by continued fine-tuning. Conclusion: The ensemble estimated adult sex, age, height, and weight from CT-derived DRRs, with generally lower errors in examinations with broader anatomical coverage.

Problem

• sex estimation
• age estimation
• height estimation
• weight estimation
• digitally reconstructed radiographs

Innovation

• deep learning ensemble
• digitally reconstructed radiograph
• multitask estimation
• body anthropometry
• cross-institutional generalization