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Accurate Differentiation of Spinal Tuberculosis and Spinal Metastases Using MR-Based Deep Learning Algorithms
Shuo Duan1, Weijie Dong2, Yichun Hua3
1Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Infection and Drug Resistance
|July 10, 2023
Summary
Deep learning models accurately distinguish spinal tuberculosis from spinal metastases using MRI scans. These AI tools show diagnostic performance comparable to experienced spine surgeons.
Area of Science:
- Radiology
- Artificial Intelligence
- Spinal Imaging
Background:
- Spinal tuberculosis (STB) and spinal metastases (SM) present similar imaging features on MRI, complicating diagnosis.
- Accurate differentiation is crucial for appropriate treatment and patient outcomes.
Purpose of the Study:
- To evaluate the efficacy of deep learning (DL) models in differentiating STB from SM using T2 sagittal MR images.
- To compare the diagnostic performance of DL models against experienced spine surgeons.
Main Methods:
- Retrospective analysis of 121 patients with confirmed STB or SM from four institutions.
- Development and validation of four DL models (MVITV2, EfficientNet-B3, ResNet101, ResNet34) using T2 sagittal MRI.
- Evaluation of model performance using accuracy, F1 score, and AUC; external validation and comparison with spine surgeon diagnoses.
Main Results:
- The MVITV2 DL model achieved the highest internal validation performance (ACC: 98.7%, F1: 98.6%, AUC: 0.98) and external test performance (ACC: 91.9%, F1: 91.5%, AUC: 0.95).
- DL models demonstrated superior diagnostic accuracy compared to a less experienced surgeon (73.7%) and approached that of an experienced surgeon (88.9%).
- Gradient-Class Activation Maps visualized high-dimensional features aiding model interpretability.
Conclusions:
- Deep learning models applied to T2 sagittal MRI can effectively discriminate between STB and SM.
- The diagnostic performance of these DL models is comparable to that of experienced spine surgeons, offering a valuable tool for clinical decision-making.
