Work

Intelligent anatomy of spinal tuberculosis: region-specific performance analysis of a deep learning model based on multi-center CT axial images

Year: 2026

Type: article

Abstract: Objective To solve the problems of insufficient model generalization ability and unclear influence of anatomical regional differences in the intelligent diagnosis of spinal tuberculosis, and to construct a multi-center CT deep learning model.Method This study adopted a retrospective multi-center design and collected CT images of 1025 patients with spinal tuberculosis from 4 hospitals in the southwest region from 2012 to 2024. In the preprocessing stage, DICOM is converted to 224x224 RGB format and marked by senior radiologist. A binary classification model was constructed based on ResNet50 transfer learning and trained for 100 rounds using the Adam optimizer (lr = 1e-4, batch = 8). The verification framework is divided into three-stage validation framework: Center 1, 80% training of data/verification within 20%; Centers 2 to 4 were combined into the external test set and subdivided by cervical vertebrae/thoracic vertebrae/lumbar vertebrae/sacrum vertebrae. The evaluation indicators include AUC, accuracy, precision, recall and F1 score, supplemented by Grad-CAM visualization.Result The internal validation performance of the model was excellent (AUC = 0.90), but the external test decreased (AUC = 0.88). Anatomical stratification showed that the cervical vertebrae were the weakest (AUC = 0.70), the lumbar vertebrae were the most stable (AUC = 0.91), the sacral vertebrae had high sensitivity (recall rate = 0.756), and the thoracic vertebrae had a high false positive rate.Conclusion The anatomical dependence of diagnostic performance was revealed for the first time, and tiered deployment strategy is suggested: priority deployment of high-stability segments (lumbar vertebrae/sacrum), low-efficiency regions (cervical vertebrae) combined with MRI verification, and hierarchical data standards were established to optimize the algorithm.

Source: Critical Public Health

DOI: 10.1080/09581596.2026.2670803