Year: 2026
Type: article
Abstract: 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.
Source: Frontiers in Medicine
Author: Panyi Yang
Institutions: Sichuan University, West China Hospital of Sichuan University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7124280471