Explainable AI for classifying vertebral fracture histology in digital spine pathology

Introduction Vertebral compression fractures (VCFs) commonly arise from osteoporosis, trauma, or malignancy. Accurate subtype differentiation is clinically essential but remains challenging using conventional imaging and histology. Methods We developed an explainable AI-driven digital pathology pipeline integrating whole-slide histopathology with clinical metadata and transcriptomic profiles to support fracture subtype classification, risk stratification, and therapy prediction. Model interpretability was assessed using Grad-CAM heatmaps and SHAP analysis, and the multi-omics risk score was validated across independent cohorts. Results The deep learning classifier achieved 86–91% accuracy (F1 score 0.83–0.88) for osteoporotic, traumatic, and neoplastic fractures, despite modest per-class AUCs (0.49–0.54). Grad-CAM and SHAP highlighted biologically meaningful cues, including trabecular thinning, nuclear atypia, and marrow fibrosis. The multi-omics risk score stratified outcomes: high-risk fractures showed upregulated TNF–NF-κB signaling, reduced cytotoxic T-cell infiltration, and significantly worse 3-year survival (log-rank p < 0.001). Drug sensitivity modeling predicted response patterns, with low-risk patients aligned with bisphosphonates and RANKL inhibitors and high-risk cases associated with resistant phenotypes. Discussion This pipeline combines diagnostic performance with transparent interpretability and operates efficiently on modest computing resources, supporting telepathology deployment in resource-limited settings. By uniting classification, biological insight, and scalable implementation, the framework advances AI-enabled digital pathology toward more equitable global healthcare delivery.