Automatic adult age estimation using bone mineral density of proximal femur via deep learning.
Authors
Affiliations (5)
Affiliations (5)
- Institute of Forensic Science, Fudan University, Shanghai, China.
- Medical Imaging Department, 3201 Hospital of Xi'an Jiaotong University Health Science Center, Hanzhong, Shannxi, China.
- Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address: [email protected].
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China. Electronic address: [email protected].
- Institute of Forensic Science, Fudan University, Shanghai, China. Electronic address: [email protected].
Abstract
Accurate adult age estimation (AAE) is critical for forensic and anthropological applications, yet traditional methods relying on bone mineral density (BMD) face significant challenges due to biological variability and methodological limitations. This study aims to develop an end-to-end Deep Learning (DL) based pipeline for automated AAE using BMD from proximal femoral CT scans. The main objectives are to construct a large-scale dataset of 5151 CT scans from real-world clinical and cadaver cohorts, fine-tune the Segment Anything Model (SAM) for accurate femoral bone segmentation, and evaluate multiple convolutional neural networks (CNNs) for precise age estimation based on segmented BMD data. Model performance was assessed through cross-validation, internal clinical testing, and external post-mortem validation. SAM achieved excellent segmentation performance with a Dice coefficient of 0.928 and an average intersection over union (mIoU) of 0.869. The CNN models achieved an average mean absolute error (MAE) of 5.20 years in cross-validation (male: 5.72; female: 4.51), which improved to 4.98 years in the independent clinical test set (male: 5.32; female: 4.56). External validation on the post-mortem dataset revealed an MAE of 6.91 years, with 6.97 for males and 6.69 for females. Ensemble learning further improved accuracy, reducing MAE to 4.78 years (male: 5.12; female: 4.35) in the internal test set, and 6.58 years (male: 6.64; female: 6.37) in the external validation set. These findings highlight the feasibility of dl-driven AAE and its potential for forensic applications, offering a fully automated framework for robust age estimation.