Spatiotemporal analysis of small bowel capsule endoscopy videos for outcomes prediction in Crohn's disease.

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.

Diagnostic significance of mesenteric lymph node involvement in proximal small bowel Crohn's disease.

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.

Association of Infliximab and Vedolizumab Trough Levels with Reported Rates of Adverse Events: A Cross-Sectional Study.

Infliximab and vedolizumab are effective treatments for inflammatory bowel disease (IBD), although associated with adverse events (AE). While low or non-existent drug levels and positive antidrug antibodies have been associated with therapeutic failure, there is no clear association between higher drug levels and AE. A cross-sectional study consisting of Crohn's disease (CD) and ulcerative colitis (UC) patients receiving infliximab or vedolizumab at the Sheba Medical Center was performed. Patients completed a questionnaire regarding AEs related to biological therapy. Serum trough levels obtained on the same day were analyzed. Objective measures of outcomes were retrieved from medical records. Questionnaires were completed by infliximab (n = 169) and vedolizumab (n = 88)-treated therapy patients. Higher infliximab levels were only numerically associated with the occurrence of at least one AE (p = 0.08). When excluding fatigue and abdominal pain, higher infliximab levels were statistically associated with the occurrence of at least one AE (p = 0.03). Vedolizumab drug levels > 18 µg/mL were also linked with the occurrence of more AEs. No specific association was observed between the increased levels of either infliximab or vedolizumab and specific AEs (neurological symptoms, upper GI symptoms, infectious complications, and musculoskeletal symptoms). As significant AEs are very rare, additional multi-center studies are required.

Automated Detection of Crohn's Disease Intestinal Strictures on Capsule Endoscopy Images Using Deep Neural Networks.

BACKGROUND AND AIMS: Passable intestinal strictures are frequently detected on capsule endoscopy [CE]. Such strictures are a major component of inflammatory scores. Deep neural network technology for CE is emerging. However, the ability of deep neural networks to identify intestinal strictures on CE images of Crohn's disease [CD] patients has not yet been evaluated. METHODS: We tested a state-of-the-art deep learning network for detecting CE images of strictures. Images of normal mucosa, mucosal ulcers, and strictures of Crohn's disease patients were retrieved from our previously described CE image bank. Ulcers were classified as per degree of severity. We performed 10 cross-validation experiments. A clear patient-level separation was maintained between training and testing sets. RESULTS: Overall, the entire dataset included 27 892 CE images: 1942 stricture images, 14 266 normal mucosa images, and 11 684 ulcer images [mild: 7075, moderate: 2386, severe: 2223]. For classifying strictures versus non-strictures, the network exhibited an average accuracy of 93.5% [±6.7%]. The network achieved excellent differentiation between strictures and normal mucosa (area under the curve [AUC] 0.989), strictures and all ulcers [AUC 0.942], and between strictures and different grades of ulcers [for mild, moderate, and severe ulcers-AUCs 0.992, 0.975, and 0.889, respectively]. CONCLUSIONS: Deep neural networks are highly accurate in the detection of strictures on CE images in Crohn's disease. The network can accurately separate strictures from ulcers across the severity range. The current accuracy for the detection of ulcers and strictures by deep neural networks may allow for automated detection and grading of Crohn's disease-related findings on CE.