A 2.5D Transformer-based multi-modal fusion framework for differentiating between brucellar spondylitis and spinal tuberculosis: a multicenter study

Differentiating Brucellar Spondylitis (BS) from Spinal Tuberculosis (STB) remains a clinical conundrum due to their overlapping radiological and clinical manifestations. Accurate early diagnosis is critical for determining appropriate antibiotic regimens and surgical interventions. This study aims to develop a non-invasive, multi-modal diagnostic framework utilizing 2.5D deep learning and Transformer mechanisms as a potential adjunct to assist differentiation. In this retrospective multicenter study, clinical and MRI data from 238 patients (169 BS, 69 STB) were analyzed, with class imbalance addressed during model development. Data from Center A ( n = 198) were divided into training ( n = 138) and internal validation ( n = 60) sets; Center B provided an external test set ( n = 40). We developed a multimodal differential diagnosis model that integrated clinical features, MRI radiomics, and a 2.5D deep learning network. Lesions were segmented (3D Slicer), radiomic features were extracted and screened, and a 2.5D model was constructed. A Transformer architecture then achieved deep fusion of multimodal features. Performance was evaluated using metrics such as the area under the curve (AUC), sensitivity, and specificity. ResNet18 demonstrated the best performance in slice-level prediction, achieving AUCs of 0.863, 0.766, and 0.791 in the training, validation, and test cohorts, respectively. The Transformer fusion model showed outstanding performance, achieving AUCs of 0.901 in the validation cohort, significantly outperforming multiple instance learning (0.812) and ensemble fusion (0.807). The final combined model yielded AUCs of 0.957 (95% CI: 0.928–0.986) in training, 0.916 (95% CI: 0.834–0.998) in validation, and 0.896 (95% CI: 0.797–0.994) in test, showing the most balanced performance. Calibration curves and decision curves validated the model’s reliability and potential clinical utility. Compared with traditional methods, the proposed 2.5D Transformer-based multimodal model demonstrated superior performance in differentiating BS from STB. Developed and validated with multi-center data, this framework offers a promising non-invasive adjunctive means to facilitate timely and accurate treatment decision-making in endemic regions, though prospective validation is warranted to further establish its generalizability.