EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT
This work addresses a critical diagnostic problem for clinicians to avoid harmful treatment mismanagement, but it appears incremental as it builds on existing methods like ResNet and CTFoundation.
The researchers tackled the challenge of differentiating Crohn's disease from intestinal tuberculosis using 3D CTE scans and machine learning, achieving improved accuracy by identifying specific biomarkers and applying techniques like XGBoost with SHAP analysis, though no concrete numbers were provided.
Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - particularly granulomas, making it challenging to clinically differentiate them. Our research leverages 3D CTE scans, computer vision, and machine learning to improve this differentiation to avoid harmful treatment mismanagement such as unnecessary anti-tuberculosis therapy for Crohn's disease or exacerbation of tuberculosis with immunosuppressants. Our study proposes a novel method to identify radiologist - identified biomarkers such as VF to SF ratio, necrosis, calcifications, comb sign and pulmonary TB to enhance accuracy. We demonstrate the effectiveness by using different ML techniques on the features extracted from these biomarkers, computing SHAP on XGBoost for understanding feature importance towards predictions, and comparing against SOTA methods such as pretrained ResNet and CTFoundation.