Can artificial intelligence-derived bone mineral density outperform traditional morphometric measurements in
Chia-Che Lee1,2, Xi-Qian Heng3, Hung-Kuan Yen2
1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Objectives:
To investigate whether an artificial intelligence (AI) model could demonstrate high diagnostic accuracy in identifying dual-energy X-ray absorptiometry (DXA) confirmed osteoporosis, outperform traditional human-read metrics, and deliver substantial clinical net benefit.
Methods:
This retrospective multicenter cohort study included 382 patients with hip fractures who had both preoperative pelvic radiographs and postoperative DXA scans. An AI model analyzed the intact contralateral proximal femur to predict both bone mineral density (BMD) values and T-scores. Diagnostic performance was evaluated against DXA-confirmed osteoporosis (T-score ≤-2.5) and compared with manual cortical thickness index (CTI) and femoral neck width (FNW) measurements using Spearman rank-order correlation analysis, receiver operating characteristic (ROC) curves, the DeLong test, and decision curve analysis (DCA).
Results:
The AI model showed a strong correlation with DXA-derived BMD values for both the femoral neck (ρ = 0.76, R2 = 0.55, mean absolute error (MAE) = 0.06 g/cm2) and the total hip (ρ = 0.69, R2 = 0.51, MAE = 0.08 g/cm2). Correspondingly, the AI-predicted T-scores correlated well with DXA-derived T-scores for the femoral neck (ρ = 0.76, R2 = 0.55, MAE = 0.47) and the total hip (ρ = 0.69, R2 = 0.51, MAE = 0.65). The AI model demonstrated excellent discriminative performance, achieving an area under the ROC curve (AUC) of 0.88 (95% confidence interval [CI] 0.83-0.93) for the femoral neck and 0.87 (95% CI 0.82-0.92) for the total hip, both significantly higher than CTI (0.72 [95% CI 0.65-0.79]; P < 0.001) and FNW (0.53 [95% CI 0.44-0.62]; P < 0.001). DCA illustrated that the AI models provided greater clinical net benefit than manual measurements, remaining clinically advantageous up to a threshold probability exceeding 0.90.
Conclusions:
Deep learning AI offers a superior, automated alternative to traditional manual morphometric measurements for identifying osteoporosis on routine pelvic radiographs in patients with hip fractures, potentially facilitating timely secondary fracture prevention.
