Performance of a Self-Supervised Pretrained Neural Network for Orthopedic Radiograph Classification

Abstract Purpose To evaluate whether domain-adaptive self-supervised pretraining on musculoskeletal radiographs improves fracture classification and attribution faithfulness relative to ImageNet-pretrained baselines. Materials and Methods This study (June 2025–May 2026) used previously acquired radiographs to compare three ResNet-50 initializations: supervised ImageNet pretraining (control), self-supervised ImageNet pretraining (DINO), and DINO with additional domain-adapted pretraining on 44,029 musculoskeletal radiographs (DINO-Ortho). All models underwent supervised fine-tuning in three experiments: in-distribution (MURA and FracAtlas datasets), out-of-distribution (an external dataset of 5,365 calcaneal radiographs from 1,775 patients), and initial weights (calcaneal radiographs only). Metrics included sensitivity, specificity, test accuracy, area under the receiver operating characteristic curve (AUROC), and Cohen’s kappa; attribution faithfulness was quantified using Remove and Debias scores from Grad-CAM saliency maps. Comparisons used DeLong and Friedman tests. Results Classification performance did not differ significantly between DINO-Ortho and either baseline in any experiment (DINO-Ortho AUROC, 0.89 in-distribution and 0.95 with initial weights). All three models discriminated poorly out-of-distribution (control, 0.59; DINO, 0.57; DINO-Ortho, 0.58). DINO-Ortho showed significantly higher attribution faithfulness than both baselines in all three experiments, including out-of-distribution (25.39 vs −10.41 and 2.14; P < .001) and initial weights (20.88 vs 11.51 and 1.27; P < .001). Qualitative rankings favored DINO-Ortho but did not differ significantly. Conclusion Domain-adapted self-supervised pretraining on musculoskeletal radiographs improved attribution faithfulness while maintaining classification performance comparable to ImageNet-pretrained baselines; no model generalized adequately to external radiographs without task-specific fine-tuning.