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INTRODUCTION: Patency capsule (PC) is a recommended procedure to rule out small bowel stenosis before video capsule endoscopy (VCE). We examined future clinical outcomes among patients with a failed PC vs patients in whom the PC had passed (passed PC). METHODS: A post hoc analysis of 2 prospective cohort studies of adult patients with quiescent small bowel Crohn’s disease (CD) who underwent PC between 2013 and 2020. The primary composite outcome was the need for intestinal surgery or endoscopic dilation during follow-up in patients with or without a failed PC. RESULTS: A total of 190 patients were included (47: failed PC and 143: passed PC, median follow-up 34.12 months). Patients with a failed PC had higher rates of the primary composite outcome (21.3% vs 1.4%, hazard ratio [HR] 20.3, 95% confidence interval [CI] 4.4–93.7, P < 0.001) and also secondary outcomes including intestinal surgery (14.9% vs 0.70%, P < 0.001), endoscopic dilation (14.9% vs 0.70%, P < 0.001), admissions (23.3% vs 5.7%, P < 0.001), and clinical flares (43.9% vs 27.7%, P 5 0.005) during follow-up compared with controls. Failed PC was the only statistically significant factor for surgery and/or endoscopic dilation, regardless of a B2/B3 phenotype at baseline. In sensitivity analyses restricted only to patients with a stricturing phenotype (n 5 73), a failed PC still predicted the long-term composite outcome (HR 8.68, 95% CI 1.72–43.68, P 5 0.002). Of the 190 patients ingesting a PC, only 1 patient with a failed PC had 48 hours of self-limiting mild symptoms. DISCUSSION: Patients with clinically stable CD with a failed PC have worse long-term clinical outcomes than those without, independently of the CD phenotype. Standalone PC may serve as a novel, safe, and affordable prognostic examination to identify patients with quiescent CD who have a higher risk for future worse clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of proximal small bowel involvement in Crohn's disease (CD) can be challenging at magnetic resonance enterography (MRE). The inflammatory process in CD can be associated with peri-intestinal inflammatory reactions, including the presence of inflamed mesenteric lymph nodes. Objectives: To evaluate the significance of inflamed mesenteric lymph nodes adjacent to the jejunum at MRE in CD and the association with proximal bowel disease as detected by video capsule endoscopy (VCE). Design: This retrospective study was performed in two tertiary medical centres, and included 64 patients with CD who underwent MRE as well as VCE within 1 year. Methods: Data were collected for examinations performed between August 2013 and February 2021. MRE images were independently reviewed by radiologists who were blinded to the clinical data. Association between the presence of mesenteric lymph nodes adjacent to jejunum at MRE and disease activity according to VCE Lewis scores of proximal small bowel was examined. Results: VCE detected proximal disease in 24/64 patients (37.5%). Presence of regional lymph nodes in the jejunal mesentery was significantly associated with jejunal disease as seen on VCE (p < 0.001). Of the 20 patients who had proximal mesenteric lymph nodes at MRE, 15 (75%) had jejunal disease at VCE (sensitivity, 62.5%; specificity, 87.5%; and negative and positive predictive values, 79.5 and 75%, respectively). The number of regional lymph nodes was positively correlated with jejunal disease (mean: 2.63 ± 2.90 versus 0.78 ± 2.60, p = 0.01). Other MRE features of lymph nodes were not significantly predictive of jejunal CD. Conclusion: In patients with CD, inflamed regional lymph nodes in the jejunal mesentery at MRE can be valuable to suggest proximal small bowel disease, even when bowel wall features at imaging do not suggest disease involvement. Plain language summary: The diagnosis of proximal small bowel involvement in Crohn's disease (CD) can be challenging at magnetic resonance enterography (MRE). We analysed MRE examinations in patients with CD for the presence of lymph nodes adjacent to the proximal small bowel. We included 64 patients with CD who had MRE examinations and video capsule endoscopy (VCE) examinations within 1 year. Of 64 patients, 24 had proximal small bowel disease according to VCE. We found that of 20 patients who had regional mesenteric lymph nodes in the jejunal mesentery at MRE, 15 had proximal bowel disease involvement. We also found that patients with jejunal disease had a larger number of regional lymph nodes compared to patients without jejunal disease. All but one patient had normal appearing bowel at MRE. But, using regional mesenteric lymphadenopathy at MRE as an indicator for disease, 15/24 (62.5%) patients with proximal small bowel disease were detected. We therefore conclude that regional mesenteric lymph nodes assessment at MRE can aid diagnose proximal bowel disease, even when the proximal bowel looks normal at imaging. Presence of proximal mesenteric lymph nodes at MRE in patients with CD possibly warrant further investigation of the proximal small bowel by endoscopic measures. [ABSTRACT FROM AUTHOR]

Background: Video capsule endoscopy (VCE) has been proven to accurately diagnose small-bowel inflammation and predict flares among patients with quiescent Crohn's disease (CD). However, data regarding its predictive role in this population over an extended follow-up are scarce. Objectives: To predict clinical exacerbation and to assess the yield of Lewis score in identifying CD patients with future clinical exacerbation during an extended follow-up (>24 months). Design: A post hoc analysis study. Methods: Adult patients with quiescent small-bowel CD who were followed with VCE, inflammatory biomarkers and magnetic resonance enterography in a prospective study (between 2013 and 2018). We extracted extended clinical data (up to April 2022). The primary composite outcome (i.e. clinical exacerbation) was defined as intestinal surgery, endoscopic dilation, CD-related admission, corticosteroid administration, or biological/immunomodulator treatment change during follow-up. Results: Of the 61 patients in the study [median age 29 (24–37) years, male 57.4%, biologic treatment 46.7%], 18 patients met the primary outcome during an extended follow-up [median 58.0 (34.5–93.0) months]. On univariable analysis, complicated [hazard ratio (HR) 7.348, p = 0.002] and stricturing disease phenotype (HR 5.305, p = 0.001) were associated with higher risk for clinical exacerbation during follow-up. A baseline VCE middle small-bowel segment Lewis score (midLS) ⩾ 135 identified patients with future exacerbation [AUC (area under the curve) 0.767, 95% confidence interval (CI) 0.633–0.902, p = 0.001, HR 6.317, 93% negative predictive value], whereas the AUC of the conventional Lewis score was 0.734 (95% CI: 0.589–0.879, p = 0.004). Sensitivity analysis restricted to patients with either complicated (n = 34) or stricturing (n = 26) disease phenotype revealed that midLS still predicted clinical exacerbation during follow-up (AUC 0.747/0.753, respectively), in these patients. Conclusion: MidLS predicts treatment failure in quiescent CD patients (median follow-up of 5 years) independently of disease phenotype. [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]

Background: Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined. Objectives: We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients. Design: This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software. Methods: CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis. Results: The patient cohort included 101 patients. The median duration of follow-up was 902 (354–1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74. Conclusion: Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD. [ABSTRACT FROM AUTHOR]

Background: Video capsule endoscopy (VCE) has been proven to accurately diagnose small-bowel inflammation and predict flares among patients with quiescent Crohn’s disease (CD). However, data regarding its predictive role in this population over an extended follow-up are scarce. Objectives: To predict clinical exacerbation and to assess the yield of Lewis score in identifying CD patients with future clinical exacerbation during an extended follow-up (>24 months). Design: A post hoc analysis study. Methods: Adult patients with quiescent small-bowel CD who were followed with VCE, inflammatory biomarkers and magnetic resonance enterography in a prospective study (between 2013 and 2018). We extracted extended clinical data (up to April 2022). The primary composite outcome (i.e. clinical exacerbation) was defined as intestinal surgery, endoscopic dilation, CD-related admission, corticosteroid administration, or biological/immunomodulator treatment change during follow-up. Results: Of the 61 patients in the study [median age 29 (24-37) years, male 57.4%, biologic treatment 46.7%], 18 patients met the primary outcome during an extended follow-up [median 58.0 (34.5-93.0) months]. On univariable analysis, complicated [hazard ratio (HR) 7.348, 푝 = 0.002] and stricturing disease phenotype (HR 5.305, 푝 = 0.001) were associated with higher risk for clinical exacerbation during follow-up. A baseline VCE middle small-bowel segment Lewis score (midLS) ⩾ 135 identified patients with future exacerbation [AUC (area under the curve) 0.767, 95% confidence interval (CI) 0.633-0.902, 푝 = 0.001, HR 6.317, 93% negative predictive value], whereas the AUC of the conventional Lewis score was 0.734 (95% CI: 0.589- 0.879, 푝 = 0.004). Sensitivity analysis restricted to patients with either complicated (푛 = 34) or stricturing (푛 = 26) disease phenotype revealed that midLS still predicted clinical exacerbation during follow-up (AUC 0.747/0.753, respectively), in these patients. Conclusion: MidLS predicts treatment failure in quiescent CD patients (median follow-up of 5 years) independently of disease phenotype. [ABSTRACT FROM AUTHOR]

Background: Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn’s disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined. Objectives: We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients. Design: This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software. Methods: CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis. Results: The patient cohort included 101 patients. The median duration of follow-up was 902 (354–1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74. Conclusion: Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD. [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]

(1) Background: Villous atrophy is an indication for small bowel capsule endoscopy (SBCE). However, SBCE findings are not described uniformly and atrophic features are sometimes not recognized; (2) Methods: The Delphi technique was employed to reach agreement among a panel of SBCE experts. The nomenclature and definitions of SBCE lesions suggesting the presence of atrophy were decided in a core group of 10 experts. Four images of each lesion were chosen from a large SBCE database and agreement on the correspondence between the picture and the definition was evaluated using the Delphi method in a broadened group of 36 experts. All images corresponded to histologically proven mucosal atrophy; (3) Results: Four types of atrophic lesions were identified: mosaicism, scalloping, folds reduction, and granular mucosa. The core group succeeded in reaching agreement on the nomenclature and the descriptions of these items. Consensus in matching the agreed definitions for the proposed set of images was met for mosaicism (88.9% in the first round), scalloping (97.2% in the first round), and folds reduction (94.4% in the first round), but granular mucosa failed to achieve consensus (75.0% in the third round); (4) Conclusions: Consensus among SBCE experts on atrophic lesions was met for the first time. Mosaicism, scalloping, and folds reduction are the most reliable signs, while the description of granular mucosa remains uncertain. [ABSTRACT FROM AUTHOR]