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Introduction: Complicated perianal disease (cPD) may be the sole presentation of Crohn's disease (CD). The role of small-bowel capsule endoscopy (SBCE) in the diagnostic algorithm of cPD is unclear. We aimed to evaluate the role of SBCE as a diagnostic tool, in patients with cPD, after a negative standard workup for CD. Methods: A multicenter, retrospective, cross-sectional study, in patients with cPD, and negative standard workup for CD (ileocolonoscopy and cross-sectional imaging), who underwent SBCE for suspected CD. Demographics, biomarkers, and the Lewis Score (LS) were recorded and analyzed. An LS ≥ 135 was considered a positive SBCE for diagnosing CD. Results: Ninety-one patients were included: 65 (71.4%) males; median age: 37 (29–51) years; cPD duration: 25.1 (12.5–66.1) months. Positive SBCE: 24/91 (26.4%) patients. Fecal calprotectin (FC) positively correlated with LS (r = 0.81; p < 0.001). FC levels of 100 µg/g and 50 µg/g had a sensitivity of only 40% and 55% to rule out small-bowel CD, with a negative predictive value (NPV) of only 76% and 80%, respectively. Conclusions: SBCE contributed to CD diagnosis in a quarter of patients with cPD after a negative standard workup. FC levels correlated with the degree of inflammation defined by the LS. However, the NPV of FC was low, suggesting that SBCE should be considered for patients with cPD even after a negative standard workup. [ABSTRACT FROM AUTHOR]

Background and Aims: The aim of our study was to create an accurate patient-level combined algorithm for the identification of ulcers on CE images from two different capsules. Methods: We retrospectively collected CE images from PillCam-SB3′s capsule and PillCam-Crohn's capsule. ML algorithms were trained to classify small bowel CE images into either normal or ulcerated mucosa: a separate model for each capsule type, a cross-domain model (training the model on one capsule type and testing on the other), and a combined model. Results: The dataset included 33,100 CE images: 20,621 PillCam-SB3 images and 12,479 PillCam-Crohn's images, of which 3582 were colonic images. There were 15,684 normal mucosa images and 17,416 ulcerated mucosa images. While the separate model for each capsule type achieved excellent accuracy (average AUC 0.95 and 0.98, respectively), the cross-domain model achieved a wide range of accuracies (0.569–0.88) with an AUC of 0.93. The combined model achieved the best results with an average AUC of 0.99 and average mean patient accuracy of 0.974. Conclusions: A combined model for two different capsules provided high and consistent diagnostic accuracy. Creating a holistic AI model for automated capsule reading is an essential part of the refinement required in ML models on the way to adapting them to clinical practice. [ABSTRACT FROM AUTHOR]