Explainable opportunistic osteoporosis screening from chest X-rays: a retrospective comparison of foundation models.

Abstract

We evaluated foundation models for opportunistic osteoporosis screening from chest X-rays using a novel explainability framework. DINOv2 with low-rank adaptation achieved the best performance (AUC 0.93) while demonstrating clear clinical reasoning. Our findings highlight that explainability should be prioritized alongside accuracy in medical AI, enhancing trust in clinical deployment. Deep learning models show promise for opportunistic osteoporosis screening from chest X-rays but have traditionally relied on convolutional neural networks with limited explainability. This study introduces a quantitative framework for explainability evaluation and systematically compares diverse foundation models to identify an optimal balance between performance and explainability. In this retrospective study, a retrospective dataset comprising 21,031 chest X-rays paired with bone mineral density scores from 14,502 female patients at Seoul National University Hospital was used. Twelve foundation model variants-combinations of natural- and medical-domain models fine-tuned using various strategies-were trained to classify osteoporosis status (normal, osteopenia, or osteoporosis). Foundation models were evaluated based on predictive performance (AUC, accuracy, sensitivity, and specificity) and explainability, assessed through occlusion analysis (AUC change after bone perturbation, <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>Δ</mi> <mrow><mi>bone</mi></mrow> </msub> </math> ) and saliency-map analysis (overlap between bone regions and saliency maps, <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mtext>IoU</mtext> <mrow><mi>bone</mi></mrow> </msub> </math> ). DINOv2, fine-tuned with low-rank adaptation, achieved the highest predictive performance (AUC of 0.93; 95% CI, 0.92-0.94) and demonstrated robust explainability by focusing on clinically relevant bone structures, such as the spine and ribs. In osteoporosis screening from chest X-rays, statistical analysis showed that medical foundation models did not consistently outperform natural-domain models, and higher performance did not always correlate with better explainability. Our findings underscore the necessity of incorporating explainability as a key criterion when selecting deep learning models for opportunistic osteoporosis screening. Furthermore, the proposed framework can be readily extended to other medical tasks, fostering the development of more trustworthy and interpretable AI-assisted screening tools.