Explainable AI Models on Radiographic Images Integrated with Clinical Measurements: Prediction for Unstable Hips in Infants

Abstract

Abstract Considering explainability is crucial in medical artificial intelligence, technologies to quantify Grad-CAM heatmaps and perform automatic integration based on domain knowledge remain lacking. Hence, we created an end-to-end model that produced CAM scores on regions of interest (CSoR), a measure of relative CAM activity, and feature importance scores by automatic algorithms for clinical measurement (aaCM) followed by LightGBM. In this multicenter research project, the diagnostic performance of the model was investigated with 813 radiographic hip images in infants at risk of unstable hips, with the ground truth defined by provocative examinations. The results indicated that the accuracy of aaCM was higher than that of specialists, and the model with ad hoc adoption of aaCM outperformed the image-only-based model. Subgroup analyses in positive cases indicated significant differences in CSoR between the unstable and contralateral sides despite containing only binary labels (positive or negative). In conclusion, aaCM reinforces the performance, and CSoR potentially indicates model reliability.