ArticleScientific reports2022
Differentiation of intestinal tuberculosis and Crohn's disease through an explainable machine learning method.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Urinary lipoarabinomannan for gastrointestinal tuberculosis: Another tool in the kit.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Article
- An Integrated Review of Conventional and Emerging Diagnostic and Therapeutic Modalities to Reduce the Risk of Surgical Resections in Intestinal Tuberculosis.Diagnostics (Basel, Switzerland) · 2026Review
- The Overlap Between Crohn's Disease and Intestinal Tuberculosis: A Never-Ending Story.Medicina (Kaunas, Lithuania) · 2026Review
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances.World journal of gastroenterology · 2025Review
- Deep learning model to differentiate Crohn's disease from intestinal tuberculosis using histopathological whole slide images from intestinal specimens.Virchows Archiv : an international journal of pathology · 2024Article
- A Machine Learning Method for Differentiation Crohn's Disease and Intestinal Tuberculosis.Journal of multidisciplinary healthcare · 2024Article
- Article
- Differentiating gastrointestinal tuberculosis and Crohn's disease- a comprehensive review.BMC gastroenterology · 2023Review
- Gastrointestinal tuberculosis: An autopsy-based study.Infectious medicine · 2023Article
- Machine learning-based causal models for predicting the response of individual patients to dexamethasone treatment as prophylactic antiemetic.Scientific reports · 2023Article
- Etiological spectrum of isolated ileo-cecal ulcers in patients with gastrointestinal symptoms.Journal of clinical and translational research · 2023Article
- Diagnostic utility of CT enterography in the evaluation of ileocecal mucosal lesions of uncertain diagnosis on ileocolonoscopy.Polish journal of radiology · 2023Article
- Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation.Frontiers in medicine · 2022Article
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8 authors.
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Abstract
Differentiation between Crohn's disease and intestinal tuberculosis is difficult but crucial for medical decisions. This study aims to develop an effective framework to distinguish these two diseases through an explainable machine learning (ML) model. After feature selection, a total of nine variables are extracted, including intestinal surgery, abdominal, bloody stool, PPD, knot, ESAT-6, CFP-10, intestinal dilatation and comb sign. Besides, we compared the predictive performance of the ML methods with traditional statistical methods. This work also provides insights into the ML model's outcome through the SHAP method for the first time. A cohort consisting of 200 patients' data (CD = 160, ITB = 40) is used in training and validating models. Results illustrate that the XGBoost algorithm outperforms other classifiers in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision and Matthews correlation coefficient (MCC), yielding values of 0.891, 0.813, 0.969, 0.867 and 0.801 respectively. More importantly, the prediction outcomes of XGBoost can be effectively explained through the SHAP method. The proposed framework proves that the effectiveness of distinguishing CD from ITB through interpretable machine learning, which can obtain a global explanation but also an explanation for individual patients.
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