Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT

Wenjun Liu; Jin Wang; Yiting Lei; Peng Liu; Zhenghan Han; Shichu Wang; Bo Liu

doi:10.2147/IDR.S482584

Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT

Infect Drug Resist. 2025 Jan 3:18:31-42. doi: 10.2147/IDR.S482584. eCollection 2025.

Authors

Wenjun Liu¹, Jin Wang², Yiting Lei¹, Peng Liu³, Zhenghan Han¹, Shichu Wang¹, Bo Liu¹

Affiliations

¹ Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People's Republic of China.
² College of Medical Informatics, Chongqing Medical University, Chongqing, People's Republic of China.
³ Department of Orthopedics, Daping Hospital, Army Medical University, Chongqing, People's Republic of China.

Abstract

Background: Early differentiation between spinal tuberculosis (STB) and acute osteoporotic vertebral compression fracture (OVCF) is crucial for determining the appropriate clinical management and treatment pathway, thereby significantly impacting patient outcomes.

Objective: To evaluate the efficacy of deep learning (DL) models using reconstructed sagittal CT images in the differentiation of early STB from acute OVCF, with the aim of enhancing diagnostic precision, reducing reliance on MRI and biopsies, and minimizing the risks of misdiagnosis.

Methods: Data were collected from 373 patients, with 302 patients recruited from a university-affiliated hospital serving as the training and internal validation sets, and an additional 71 patients from another university-affiliated hospital serving as the external validation set. MVITV2, Efficient-Net-B5, ResNet101, and ResNet50 were used as the backbone networks for DL model development, training, and validation. Model evaluation was based on accuracy, precision, sensitivity, F1 score, and area under the curve (AUC). The performance of the DL models was compared with the diagnostic accuracy of two spine surgeons who performed a blinded review.

Results: The MVITV2 model outperformed other architectures in the internal validation set, achieving accuracy of 98.98%, precision of 100%, sensitivity of 97.97%, F1 score of 98.98%, and AUC of 0.997. The performance of the DL models notably exceeded that of the spine surgeons, who achieved accuracy rates of 77.38% and 93.56%. The external validation confirmed the models' robustness and generalizability.

Conclusion: The DL models significantly improved the differentiation between STB and OVCF, surpassing experienced spine surgeons in diagnostic accuracy. These models offer a promising alternative to traditional imaging and invasive procedures, potentially promoting early and accurate diagnosis, reducing healthcare costs, and improving patient outcomes. The findings underscore the potential of artificial intelligence for revolutionizing spinal disease diagnostics, and have substantial clinical implications.

Keywords: CT imaging; deep learning; diagnostic accuracy; osteoporotic vertebral fractures; spinal tuberculosis.