用CT影像和机器学习区分克罗恩病与肠结核，避免误治。
EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT
- 提取影像生物标志物，结合多模型分析提升判别力
- 在公开数据集上达到92.3%准确率，优于现有方法
- 通过SHAP解释模型决策，助力临床可信诊断
克罗恩病与肠结核在临床、影像、内镜及组织学表现上高度重叠，尤其是肉芽肿的存在，使得两者难以鉴别。本研究利用三维增强CT（3D CTE）扫描、计算机视觉与机器学习技术，旨在提高二者鉴别精度，避免误诊误治——如对克罗恩病患者进行不必要的抗结核治疗，或使用免疫抑制剂加重结核病情。研究提出新方法，识别放射科医生定义的生物标志物，如血管-脂肪比（VF to SF ratio）、坏死、钙化、梳齿征及肺结核共病特征，并基于这些特征采用多种机器学习模型进行分类。通过XGBoost模型计算SHAP值以解析特征重要性，并与当前最优方法（如预训练ResNet、CTFoundation）进行对比，验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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