EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT
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arXiv
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| Format: | Preprint |
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2025
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| author | Gupta, Shashwat Gupta, Sarthak Agrawal, Akshan Naaz, Mahim Yadav, Rajanikanth Bagade, Priyanka |
| author_facet | Gupta, Shashwat Gupta, Sarthak Agrawal, Akshan Naaz, Mahim Yadav, Rajanikanth Bagade, Priyanka |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT Gupta, Shashwat Gupta, Sarthak Agrawal, Akshan Naaz, Mahim Yadav, Rajanikanth Bagade, Priyanka Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.00159 |