AgeMiner: A novel metadata-integrated chronological age predictor based on bone mineral density data

The estimation of chronological age based on bone mineral density (BMD) metrics for specific anatomical sites is a critical task in forensic anthropology. Although dual-energy X-ray absorptiometry (DXA) scans of the distal 1/3 of radius and ulna are widely used in large-scale osteoporosis screenings, forensic studies leveraging such data remain scarce. This study utilized a retrospective dataset (spanning ages 12-96) of 5,134 DXA scans from the distal 1/3 radius and ulna. We analyzed these DXA scans with metadata, including sex, body mass index (BMI), and osteoporosis diagnoses, to train machine learning models. Linear regression (LR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and LightGBM (LGBM) models were optimized via Bayesian cross-validation. Results indicate that the simplest model constructed solely based on BMD data + Diagnoses showed good performance with a mean absolute error (MAE) of 2.40 years. The best-performing model was the RFR model built using the combination of Female + Diagnoses, with an MAE of 2.18 years. When only considering BMI, the best model was the RFR model for the Normal weight + Diagnoses combination, with an MAE of 2.54 years. These models have been integrated into the AgeMiner tool (https://github.com/Rarapie/AgeMiner), allowing forensic users to select the optimal model according to metadata of the tested person, thereby enabling fast and end-to-end chronological age estimation. In summary, AgeMiner and its integrated ML models provide an efficient, accurate, and customizable tool for forensic age estimation in adults and the elderly.