Comparison of etiological and physiological characteristics of fecal incontinence in men and women.

Fecal incontinence (FI) is often underreported and underestimated in men. Our aims were to clarify the causes and the physiological characteristics of FI in men and to underline the differences between etiological and physiological factors in men and women diagnosed with FI. The study cohort encompassed 200 men and 200 women who underwent anatomical and physiological evaluation for FI in a tertiary referral center specializing in pelvic floor disorders. All patients underwent endoanal ultrasound and anorectal manometry. Evacuation proctography was performed in some patients. Demographic, medical, anatomical, and physiological parameters were compared between the two study groups. Urge incontinence was the most frequent type of FI in both genders. In men, anal fistula, history of anal surgeries, rectal tumors, and pelvic radiotherapy were common etiologic factors, whereas history of pelvic surgeries was more common in women. Associated urinary incontinence was reported more frequently by women. External anal sphincter defects, usually anterior, were more common in women (M: 1.5%, F: 24%, P < 0.0001), whereas internal anal sphincter defect prevalence was similar in men and women (M: 6%, F: 12%, P = 0.19). Decreased resting and squeeze pressures were less common in men (M: 29%, F: 46%, P < 0.0001: M: 44%, F: 66%, P < 0.0001). The incidence of rectal hyposensitivity was higher in men (M: 11.1%, F: 2.8%, P < 0.0001), whereas rectal hypersensitivity was higher in women (M: 5.8%, F: 10.8%, P < 0.0001). Anorectal dyssynergia was more common in men (M: 66%, F: 37%, P < 0.0001). Significantly different etiological factors and physiological characteristics for FI were found in men. Acknowledging these differences is significant and may yield better treatment options.NEW & NOTEWORTHY Fecal incontinence (FI) in men has different etiological factors when compared with women. The prevalence of internal anal sphincter defect among men with FI was similar to women. Different manometric measurements were found among men with FI: decreased anal pressures were less common among men, whereas rectal hyposensitivity and anorectal dyssynergia were more common among men.

Where does capsule endoscopy fit in the diagnostic algorithm of small bowel intussusception?

INTRODUCTION: The investigation of small bowel (SB) intussusception is variable, reflecting the lack of existing standards. The aim of this study was to understand the role of small bowel capsule endoscopy (SBCE) to investigate this pathology. METHODOLOGY: This was a retrospective multi-centre study. Patients with intussusception on SBCE and those where SBCE was carried out due to findings of intussusception on radiological investigations were included. Relevant information was collected. RESULTS: Ninety-five patients (median age 39+/-SD19.1 years, IQR 30) were included. Radiological investigations were carried out in 71 patients (74.7%) prior to SBCE with intussusception being present in 60 patients on radiological investigations (84.5%). Thirty patients (42.2%) had intussusception on radiological investigations followed by a normal SBCE. Ten patients (14.1%) had findings of intussusception on radiological investigations, a normal SBCE and repeat radiological investigations that were also normal. Abnormal findings were noted on SBCE that could explain intussusception on imaging in (16 patients) 22.5% of patients. Five patients (5.3%) underwent radiological investigations and SBCE to investigate coeliac disease and intussusception. None had associated malignancy. Four patients (4.2%) underwent SBCE to investigate familial polyposis syndromes and went on to SB enteroscopy and surgery accordingly. Most patients (n = 14; 14.8%) with intussusception on initial SBCE (without prior radiological imaging) had suspected SB bleeding (n = 10, 10.5%). Four patients (4.2%) had additional findings of a mass on CT scan and went on to have surgery. CONCLUSION: SBCE should be used to complement radiology when investigating intussusception. It is a safe non-invasive test that will minimise unnecessary surgery. Additional radiological investigations following a negative SBCE in cases of intussusception noted on initial radiological investigations are unlikely to yield positive findings. Radiological investigations following intussusception noted on SBCE in case of patients presenting with obscure gastrointestinal bleeding, may yield additional findings.

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.

Key research questions for implementation of artificial intelligence in capsule endoscopy.

Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Design: Modified three-round Delphi consensus online survey. Methods: The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. Results: Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). Conclusion: In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.

Deep Learning Multi-Domain Model Provides Accurate Detection and Grading of Mucosal Ulcers in Different Capsule Endoscopy Types.

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.

Score reproducibility and reliability in differentiating small bowel subepithelial masses from innocent bulges.

AIMS: The primary aim of this study was to assess the reliability, intra- and inter-observer variation of the SPICE, Mucosal protrusion angle (MPA) and SHYUNG scores in differentiating a subepithelial mass (SEM) from a bulge. METHODS: This retrospective multicentre study analysed the 3 scores, radiological studies, enteroscopy and/or surgical findings. RESULTS: 100 patients with a potential SEM (mean age 57.6years) were recruited with 75 patients having pathology. In patients with a SEM the mean SPICE score was 2.04 (95% CI 1.82-2.26) as compared to 1.16 (95% CI 0.81-1.51) without any pathology (AUC 0.74, p<0.001), with a fair intra-observer agreement (Kappa 0.3, p<0.001) and slight inter-observer agreement (Kappa 0.14, p<0.05). SPICE had a 37.3% sensitivity and 92.0% specificity in distinguishing between a SEM and bulge, whereas MPA<90˚ had 58.7% and 76.0% respectively, with poor intra-observer(p = 0.05) and interobserver agreement (p = 0.64). The SHYUNG demonstrated a moderate intra-observer (Kappa 0.44, p<0.001) and slight inter-observer reliability (Kappa 0.18, p<0.001). The sensitivity of an elevated SHYUNG score (≥4) in identifying a SEM was 18.7% with a specificity of 92.0% (AUC 0.71, p = 0.002). CONCLUSIONS: Though these scores are easy to use, they have, at best, slight to moderate intra and inter-observer agreement. Their overall diagnostic performances are limited.

Erratum: Scoring systems in clinical small-bowel capsule endoscopy: all you need to know!

[This corrects the article DOI: 10.1055/a-1372-4051.].

Coexistence of Takayasu's Arteritis in Patients with Inflammatory Bowel Diseases.

BACKGROUND: Takayasu's arteritis (TA) and inflammatory bowel disease (IBD) are chronic inflammatory granulomatous disorders that have rarely been concomitantly reported in case reports and small case series. OBJECTIVE: We report a series of seven cases of TA and IBD association in two referral centers with a comprehensive review of literature. METHODS: We analyzed retrospectively the electronic medical charts of TA-IBD patients at the University Hospital of São Paulo, Brazil, and at the Sheba Medical Center at Tel Aviv University, Israel. RESULTS: Overall, five patients had Crohn's disease (DC) and two had ulcerative colitis (UC), and they were mostly female and non-Asian. All patients developed IBD first and, subsequently, TA. Two underwent colectomy and one ileocecectomy due to IBD activity, while three required cardiovascular surgery due to TA activity. Most patients are currently in clinical remission of both diseases with conventional drug treatment. CONCLUSION: Although the coexistence of TA and IBD is uncommon, both seem to be strongly associated through pathophysiological pathways.

Ulcer severity grading in video capsule images of patients with Crohn's disease: an ordinal neural network solution.

BACKGROUND AND AIMS: Capsule endoscopy (CE) is an important modality for diagnosis and follow-up of Crohn's disease (CD). The severity of ulcers at endoscopy is significant for predicting the course of CD. Deep learning has been proven accurate in detecting ulcers on CE. However, endoscopic classification of ulcers by deep learning has not been attempted. The aim of our study was to develop a deep learning algorithm for automated grading of CD ulcers on CE. METHODS: We retrospectively collected CE images of CD ulcers from our CE database. In experiment 1, the severity of each ulcer was graded by 2 capsule readers based on the PillCam CD classification (grades 1-3 from mild to severe), and the inter-reader variability was evaluated. In experiment 2, a consensus reading by 3 capsule readers was used to train an ordinal convolutional neural network (CNN) to automatically grade images of ulcers, and the resulting algorithm was tested against the consensus reading. A pretraining stage included training the network on images of normal mucosa and ulcerated mucosa. RESULTS: Overall, our dataset included 17,640 CE images from 49 patients; 7391 images with mucosal ulcers and 10,249 normal images. A total of 2598 randomly selected pathologic images were further graded from 1 to 3 according to ulcer severity in the 2 different experiments. In experiment 1, overall inter-reader agreement occurred for 31% of the images (345 of 1108) and 76% (752 of 989) for distinction of grades 1 and 3. In experiment 2, the algorithm was trained on 1242 images. It achieved an overall agreement for consensus reading of 67% (166 of 248) and 91% (158 of 173) for distinction of grades 1 and 3. The classification accuracy of the algorithm was 0.91 (95% confidence interval, 0.867-0.954) for grade 1 versus grade 3 ulcers, 0.78 (95% confidence interval, 0.716-0.844) for grade 2 versus grade 3, and 0.624 (95% confidence interval, 0.547-0.701) for grade 1 versus grade 2. CONCLUSIONS: CNN achieved high accuracy in detecting severe CD ulcerations. CNN-assisted CE readings in patients with CD can potentially facilitate and improve diagnosis and monitoring in these patients.

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.

Near-fatal myocarditis complicating typhoid fever in a traveler returning from Nepal.

We report a 27-year-old traveler who returned from Nepal suffering from typhoid fever. His disease was complicated by life-threatening myocarditis and ventricular fibrillation, a rare manifestation in travelers.