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Objectives: Evaluate the ability of MR diffusion-weighted imaging (DWI) to predict patency capsule retention in Crohn's disease (CD).Methods: Clinical and imaging data were prospectively reviewed for 80 CD patients following patency capsule administration and MR-DWI under institutional review board (IRB) approval with informed consent. Two radiologists separately assessed the presence/absence of restricted diffusion in the distal ileum. Apparent diffusion coefficients (ADC) from three regions of interest on the ileal wall were averaged. The association between restricted diffusion and retention, and sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Ability of ADC to predict retention was assessed with receiver operating characteristic (ROC) curve analysis.Results: Restricted diffusion in the distal ileum was associated with capsule retention (p = 0.001, p < 0.0001). Sensitivity, specificity, PPV and NPV of restricted diffusion for capsule retention were 100.0%, 46.2%, 30.0%, 100% and 100.0%, 56.9%, 34.9%, 100%, respectively, for two radiologists. Accuracy of ADC to predict retention was high (area under the curve = 0.851, p < 0.0001). An ADC of 1.47 mm2/s showed 90.0% sensitivity and 50.0% specificity for retention.Conclusions: Sensitivity and NPV of restricted diffusion for patency capsule retention were 100%, suggesting that DWI may predict gastrointestinal tract capability to pass video camera endoscopy.Key Points: • Capsule endoscopy enables assessment of the gastrointestinal mucosa in Crohn's disease • Prior patency capsule administration is recommended to evaluate gastrointestinal tract patency • MR diffusion-weighted imaging may detect pathological constriction of the ileum • Restricted diffusion in the distal ileum was associated with capsule retention • MR-DWI may predict gastrointestinal tract capability to pass capsule endoscopy. [ABSTRACT FROM AUTHOR]

Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice. [ABSTRACT FROM AUTHOR]