Artificial intelligence and machine learning applications in abdominal tuberculosis diagnosis: A scoping review and translational roadmap

Table 2 Categorization of artificial intelligence and machine learning techniques according to research theme and representative studies in abdominal tuberculosis

Theme	Imaging/point data	Clinical/areal data	Key techniques	Representative original studies (n = 16)
Intestinal tuberculosis vs Crohn’s disease	Endoscopy images (white-light colonoscopy), CT/MRE radiomics, histopathology WSI, ATR-FTIR spectroscopy	Clinical/EHR parameters, nomograms, T-SPOT, pulmonary TB history	CNN/deep learning (U-Net, ResNet), radiomics (handcrafted + deep learning), XGBoost, few-shot learning (Xception), NLP/TextCNN (debiased), multimodal fusion, SHAP/LIME/attention maps explainability	Park et al[17], 2025; Cheng et al[18], 2024; Shu et al[19], 2024; Liu et al[21], 2024; Li et al[22], 2024; Lin et al[23], 2024; Gong et al[26], 2023; Lu et al[27], 2023; Chen et al[28], 2022; Weng et al[29], 2022; Zhu et al[30], 2021; Kim et al[31], 2021; Tong et al[32], 2020
Peritoneal tuberculosis vs carcinomatosis	CT (omental/peritoneal signs, ascites, lymph nodes, scalloping)	Omental/peritoneal imaging signs	Machine learning ensembles, radiomics (shape, first-order, texture features)	Pang et al[25], 2023
Nodal tuberculosis vs lymphoma	Contrast-enhanced CT (lymph node features)	Lymph-node morphological/textural features	Radiomics (logistic regression/SVM)	Shen et al[24], 2024
Multimodal integration	Fusion of MRE radiomics + colonoscopy images + histopathology WSI	Integrated clinical + endoscopic + radiological + pathological features	Multidisciplinary fusion models (LASSO + logistic regression), fusion correlation neural networks	Lu et al[20], 2024; Chen et al[28], 2022
Novel/emerging approaches	Few-shot endoscopic images, ATR-FTIR spectroscopy, noisy EHR text	Clinical parameters (for few-shot/explainable models)	Few-shot learning (dual transfer), spectroscopy + XGBoost/ML, debiased TextCNN (NLP), explainable AI (SHAP, LIME, integrated gradients, attention maps)	Lin et al[23], 2024; Li et al[22], 2024; Lu et al[27], 2023 (also embedded in multiple intestinal TB vs CD studies above)

CT: Computed tomography; MRE: Magnetic resonance elastography; WSI: Whole slide image; ATR-FTIR: Attenuated total reflection-Fourier transform infrared; EHR: Electronic health record; TB: Tuberculosis; CNN: Convolutional neural network; XGBoost: Extreme gradient boosting; NLP: Natural language processing; TextCNN: Text convolutional neural network; SHAP: SHapley additive exPlanations; LIME: Local interpretable model-agnostic explanations; SVM: Support vector machine; LASSO: Least absolute shrinkage and selection operator; ML: Machine learning; CD: Crohn’s disease.