AI-based fingerprint index of visceral adipose tissue for the prediction of bowel damage in patients with Crohn's disease.

The fingerprint features of visceral adipose tissue (VAT) are intricately linked to bowel damage (BD) in patients with Crohn's disease (CD). We aimed to develop a VAT fingerprint index (VAT-FI) using radiomics and deep learning features extracted from computed tomography (CT) images of 1,135 CD patients across six hospitals (training cohort, n = 600; testing cohort, n = 535) for predicting BD, and to compare it with a subcutaneous adipose tissue (SAT)-FI. VAT-FI exhibited greater predictive accuracy than SAT-FI in both training (area under the receiver operating characteristic curve [AUC] = 0.822 vs. AUC = 0.745, p = 0.019) and testing (AUC = 0.791 vs. AUC = 0.687, p = 0.019) cohorts. Multivariate logistic regression analysis highlighted VAT-FI as the sole significant predictor (training cohort: hazard ratio [HR] = 1.684, p = 0.012; testing cohort: HR = 2.649, p < 0.001). Through Shapley additive explanation (SHAP) analysis, we further quantitatively elucidated the predictive relationship between VAT-FI and BD, highlighting potential connections such as Radio479 (wavelet-HLH-first-order standard deviation)-Frequency loose stools-BD severity. VAT-FI offers an accurate means for characterizing BD, minimizing the need for extensive clinical data.