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Background and Aim: Pan-enteric capsule endoscopy [PCE] is a highly sensitive but time-consuming tool for detecting pathology. Artificial intelligence [AI] algorithms might offer a possibility to assist in the review and reduce the analysis time of PCE. This study examines the agreement between PCE assessments aided by AI technology and standard evaluations, in patients suspected of Crohn's disease [CD]., Method: PCEs from a prospective, blinded, multicentre study, including patients suspected of CD, were processed by the deep learning solution AXARO® [Augmented Endoscopy, Paris, France]. Based on the image output, two observers classified the patient's PCE as normal or suggestive of CD, ulcerative colitis, or cancer. The primary outcome was per-patient sensitivities and specificities for detecting CD and inflammatory bowel disease [IBD]. Complete reading of PCE served as the reference standard., Results: A total of 131 patients' PCEs were analysed, with a median recording time of 303 min. The AXARO® framework reduced output to a median of 470 images [2.1%] per patient, and the pooled median review time was 3.2 min per patient. For detecting CD, the observers had a sensitivity of 96% and 92% and a specificity of 93% and 90%, respectively. For the detection of IBD, both observers had a sensitivity of 97% and had a specificity of 91% and 90%, respectively. The negative predictive value was 95% for CD and 97% for IBD., Conclusions: Using the AXARO® framework reduced the initial review time substantially while maintaining high diagnostic accuracy-suggesting its use as a rapid tool to rule out IBD in PCEs of patients suspected of Crohn's disease., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., Competing Interests: Conflict of interest None., (Copyright © 2023 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.)

Background and Aims: Meckel's diverticulum (MD) may remain silent or be associated with adverse events such as GI bleeding. The main aim of this study was to evaluate indicative small-bowel capsule endoscopy (SBCE) findings, and the secondary aim was to describe clinical presentation in patients with MD., Methods: This retrospective European multicenter study included patients with MD undergoing SBCE from 2001 until July 2021., Results: Sixty-nine patients with a confirmed MD were included. Median age was 32 years with a male-to-female ratio of approximately 3:1. GI bleeding or iron-deficiency anemia was present in nearly all patients. Mean hemoglobin was 7.63 ± 1.8 g/dL with a transfusion requirement of 52.2%. Typical capsule endoscopy (CE) findings were double lumen (n = 49 [71%]), visible entrance into the MD (n = 49 [71%]), mucosal webs (n = 30 [43.5%]), and bulges (n = 19 [27.5%]). Two or more of these findings were seen in 48 patients (69.6%). Ulcers were detected in 52.2% of patients (n = 36). In 63.8% of patients (n = 44), a combination of double lumen and visible entrance into the MD was evident, additionally revealing ulcers in 39.1% (n = 27). Mean percent SB (small bowel) transit time for the first indicative image of MD was 57% of the total SB transit time., Conclusions: Diagnosis of MD is rare and sometimes challenging, and a preoperative criterion standard does not exist. In SBCE, the most frequent findings were double-lumen sign and visible diverticular entrance, sometimes together with ulcers., (Copyright © 2023 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background and Aims: Current artificial intelligence (AI)-based solutions for capsule endoscopy (CE) interpretation are proprietary. We aimed to evaluate an AI solution trained on a specific CE system (Pillcam®, Medtronic) for the detection of angiectasias on images captured by a different proprietary system (MiroCam®, Intromedic)., Material and Methods: An advanced AI solution (Axaro®, Augmented Endoscopy), previously trained on Pillcam® small bowell images, was evaluated on independent datasets with more than 1200 Pillcam® and MiroCam® still frames (equally distributed, with or without angiectasias). Images were reviewed by experts before and after AI interpretation., Results: Sensitivity for the diagnosis of angiectasia was 97.4% with Pillcam® images and 96.1% with Mirocam® images, with specificity of 98.8% and 97.8%, respectively. Performances regarding the delineation of regions of interest and the characterization of angiectasias were similar in both groups (all above 95%). Processing time was significantly shorter with Mirocam® (20.7 ms) than with Pillcam® images (24.6 ms, p<0.0001), possibly related to technical differences between systems., Conclusion: This proof-of-concept study on still images paves the way for the development of resource-sparing, "universal" CE databases and AI solutions for CE interpretation., Competing Interests: Declaration of Competing Interest Xavier Dray, Aymeric Histace and Romain Leenhardt are co-founders and shareholders of Augmented Endoscopy. Xavier Dray has received lecture fees from Bouchara Recordati, Fujifilm, Medtronic, MSD and Pfizer, and has acted as a consultant for Alfasigma, Boston Scientific, Norgine, and Pentax. Richard Makins has received travel and accommodation expenses from Intromedic to teach capsule endoscopy internationally and in the United Kingdom. Mark McAlindon has received research materials from Given Imaging, Jinshan Science, Intromedic, Ankon and has acted as a consultant for Medtronic. Robert Benamouzig is working for Alfasigma, Medtronics, and Bayer (board) The others authors do not have disclosures., (Copyright © 2021. Published by Elsevier Ltd.)

Objective: The detection of lesions during small bowel (SB) capsule endoscopy (CE) depends on the cleanliness of the intestine. Quality reporting and comparison of different preparation methods require reliable scores. Three scores known as quantitative index (QI), qualitative evaluation (QE), and overall adequacy assessment (OAA), have been proposed to assess SB cleanliness, and are sometimes used in clinical practice and in clinical trials. However, none of these scores has received any external validation. The aim of our study was to re-assess the reproducibility of these three specific scores., Methods: One-hundred-and-fifty-five complete third-generation SB-CE video recordings were extracted from a multicenter randomized controlled trial (PREPINTEST) which evaluated three modalities of SB preparation for CE. Three experts independently read the 155 SB-CE video recordings twice, in a random order, over 48 -h periods at 6-week intervals, using the QI, QE and OAA scores. Cohen's linearly weighted kappa coefficients were calculated to assess intra-observer and inter-observer agreements., Results: Intra-observer reproducibility was fair to moderate, with kappa coefficients between 0.37 and 0.46 for QI, 0.41 and 0.51 for QE, 0.41 and 0.50 for OAA. Inter-observer reproducibility was fair to substantial according to kappa coefficients between experts varying from 0.40 to 0.64, 0.29 to 0.65, and 0.52 to 0.71, for QI, QE and OAA, respectively., Conclusions: QI, QE and OAA scores, currently used for evaluation of the quality of the preparation of SB-CE, are not sufficiently reproducible. Other scores or methods are therefore needed for SB-CE cleanliness assessment., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

Background and Aims: Little is known about small-bowel (SB) capsule endoscopy (CE) in patients with a history of gastric surgery. This study aims to evaluate the feasibility and diagnostic yield (DY) of orally ingested SB-CE in patients with surgically altered gastric anatomy., Methods: Twenty-four European centers retrospectively identified patients who had SB-CE after total or subtotal gastrectomy. The primary outcome was the DY of SB-CE (intermediate P1 to highly P2 relevant findings). Secondary outcomes were gastric and SB transit times, completion, cleanliness, and adverse event rates., Results: Studied were 248 procedures from 243 patients (mean age, 62 years) with a history of partial gastrectomy (Billroth I, 13.1%; Billroth II, 34.6%), total gastrectomy (7.4%), Whipple procedure (12.8%), sleeve gastrectomy (7.2%), or gastric bypass surgery (24.7%). Obscure GI bleeding was the most frequent indication (85.1%). SB completion rate was 84.3%. One capsule retention in the SB was noted (adverse event rate, .4%). Median SB transit time was 286 minutes (interquartile range [235; 387]). Cleanliness was rated as adequate in 92.1% of procedures. After exclusion of abnormalities found at the upper anastomotic site, the DY was 43.6%, with inflammatory/ulcerated lesions observed more frequently (23.4%) than vascular lesions (21.0%)., Conclusions: SB-CE seems to be feasible and safe in selected patients with a history of major gastric surgery and comes with a high DY. The spectrum of abnormal SB findings in these patients may be different from what is known from the literature in nonoperated patients., (Copyright © 2021 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background: Cleanliness scores in small-bowel capsule endoscopy (SBCE) have poor reproducibility. The aim of this study was to evaluate a neural network-based algorithm for automated assessment of small-bowel cleanliness during capsule endoscopy., Methods: 600 normal third-generation SBCE still frames were categorized as "adequate" or "inadequate" in terms of cleanliness by three expert readers, according to a 10-point scale, and served as a training database. Then, 156 third-generation SBCE recordings were categorized in a consensual manner as "adequate" or "inadequate" in terms of cleanliness; this testing database was split into two independent 78-video subsets for the tuning and evaluation of the algorithm, respectively., Results: Using a threshold of 79 % "adequate" still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3 %, specificity of 83.3 %, and accuracy of 89.7 %. The reproducibility was perfect. The mean calculation time per video was 3 (standard deviation 1) minutes., Conclusion: This neural network-based algorithm allowing automatic assessment of small-bowel cleanliness during capsule endoscopy was highly sensitive and paves the way for automated, standardized SBCE reports., Competing Interests: Xavier Dray, Romain Leenhardt, and Aymeric Histace are cofounders and shareholders of Augmented Endoscopy. Xavier Dray has acted as a consultant for Boston Scientific and Norgine, and has presented lectures for Fujifilm, Medtronic, and Pentax. Romain Leenhardt has presented lectures for Abbvie. All other authors declare that they have no conflicts of interest., (Thieme. All rights reserved.)

Background: In the medical literature, the nomenclature and descriptions (ND) of small bowel (SB) ulcerative and inflammatory (U-I) lesions in capsule endoscopy (CE) are scarce and inconsistent. Inter-observer variability in interpreting these findings remains a major limitation in the assessment of the severity of mucosal lesions, which can impact negatively on clinical care, training and research on SB-CE., Objective: Focusing on SB-CE in Crohn's disease (CD), our aim is to establish a consensus on the ND of U-I lesions., Methods: An international panel of experienced SB-CE readers was formed during the 2016 United European Gastroenterology Week meeting. A core group of five CE and inflammatory bowel disease (IBD) experts established an Internet-based, three-round Delphi consensus but did not participate in the voting process. The core group built illustrated questionnaires, including SB-CE still frames of U-I lesions from patients with documented CD. Twenty-seven other experts were asked to rate and comment on the different proposals for the ND of the most frequent SB U-I lesions. For each round, we used a 6-point rating scale (varying from 'strongly disagree' to 'strongly agree'). The consensus was reached when at least 80 % of the voting members scored the statement within the 'agree' or 'strongly agree' categories., Results: A 100% participation rate was obtained for all the rounds. Consensual ND were reached for the following seven U-I lesions: aphthoid erosion , deep ulceration , superficial ulceration , stenosis , edema , hyperemia and denudation ., Conclusion: Considering the most frequent SB U-I lesions seen in CE in CD, a consensual ND was reached by the international group of experts. These descriptions and names are useful not only for daily practice and medical education, but also for medical research.

Background and Aims: GI angiectasia (GIA) is the most common small-bowel (SB) vascular lesion, with an inherent risk of bleeding. SB capsule endoscopy (SB-CE) is the currently accepted diagnostic procedure. The aim of this study was to develop a computer-assisted diagnosis tool for the detection of GIA., Methods: Deidentified SB-CE still frames featuring annotated typical GIA and normal control still frames were selected from a database. A semantic segmentation images approach associated with a convolutional neural network (CNN) was used for deep-feature extractions and classification. Two datasets of still frames were created and used for machine learning and for algorithm testing., Results: The GIA detection algorithm yielded a sensitivity of 100%, a specificity of 96%, a positive predictive value of 96%, and a negative predictive value of 100%. Reproducibility was optimal. The reading process for an entire SB-CE video would take 39 minutes., Conclusions: The developed CNN-based algorithm had high diagnostic performances, allowing detection of GIA in SB-CE still frames. This study paves the way for future automated CNN-based SB-CE reading softwares., (Copyright © 2019 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

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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. [ABSTRACT FROM AUTHOR]

Background: Bubbles often mask the mucosa during capsule endoscopy (CE). Clinical scores assessing the cleanliness and the amount of bubbles in the small bowel (SB) are poorly reproducible unlike machine learning (ML) solutions. We aimed to measure the amount of bubbles with ML algorithms in SB CE recordings, and compare two polyethylene glycol (PEG)-based preparations, with and without simethicone, in patients with obscure gastro-intestinal bleeding (OGIB). Patients & Methods: All consecutive outpatients with OGIB from a tertiary care center received a PEG-based preparation, without or with simethicone, in two different periods. The primary outcome was a difference in the proportions (%) of frames with abundant bubbles (>10%) along the full-length video sequences between the two periods. SB CE recordings were analyzed by a validated computed algorithm based on a grey-level of co-occurrence matrix (GLCM), to assess the abundance of bubbles in each frame. Results: In total, 105 third generation SB CE recordings were analyzed (48 without simethicone and 57 with simethicone-added preparations). A significant association was shown between the use of a simethicone-added preparation and a lower abundance of bubbles along the SB (p = 0.04). A significantly lower proportion of "abundant in bubbles" frames was observed in the fourth quartile (30.5% vs. 20.6%, p = 0.02). There was no significant impact of the use of simethicone in terms of diagnostic yield, SB transit time and completion rate. Conclusion: An accurate and reproducible computed algorithm demonstrated significant decrease in the abundance of bubbles along SB CE recordings, with a marked effect in the last quartile, in patients for whom simethicone had been added in PEG-based preparations, compared to those without simethicone. [ABSTRACT FROM AUTHOR]

Background and Aims: Capsule endoscopy (CE) is a visual modality; hence, diagnosis relies on image quality. We studied the contribution of image parameters to visualization quality and their effect on diagnostic certainty of small bowel (SB) lesions. Methods: Five clear CE images of common SB pathology – two vascular lesions, two inflammatory, one polyp – were processed for three image parameters to simulate poor SB conditions: opacity (color-matched to luminal content; 10– 90%, 10% increments); blurriness (radius 1– 10 pixels; one pixel increments); and contrast (− 50-50%; 10% increments). Nine expert readers evaluated whether images were adequate for diagnosis. Points where perception of image quality changed significantly were determined for each parameter. Three further sets of SBCE images (vascular, inflammatory, and neoplastic lesions; nine images/set) were processed for four points/parameters. Twenty experienced/expert CE readers reviewed these images. Results: The negative effects of opacity in diagnostic certainty were mostly evident in images of neoplasia; images of vascular and inflammatory lesions were less affected. Similar results were observed with increasing blur radius, simulating movement, and poor focus. The proportions of readers finding vascular and inflammatory images adequate for diagnosis did not drop significantly at wider blur radii, while images of neoplasia were quickly deemed inadequate. Low contrast had a greater negative effect than high, most consistently in neoplastic lesions. Conclusion: Poor visualization quality in all parameters affected mostly neoplastic lesions. Software to increase contrast and sharpen images can improve visualization quality; smart frame rate adaptation could improve the number of high-quality frames obtained. Thoroughness in SB cleansing is most important when there is a suspicion of neoplasia. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown promising results in digestive endoscopy, especially in capsule endoscopy (CE). However, some physicians still have some difficulties and fear the advent of this technology. We aimed to evaluate the perceptions and current sentiments toward the use of AI in CE. An online survey questionnaire was sent to an audience of gastroenterologists. In addition, several European national leaders of the International CApsule endoscopy REsearch (I CARE) Group were asked to disseminate an online survey among their national communities of CE readers (CER). The survey included 32 questions regarding general information, perceptions of AI, and its use in daily life, medicine, endoscopy, and CE. Among 380 European gastroenterologists who answered this survey, 333 (88%) were CERs. The mean average time length of experience in CE reading was 9.9 years (0.5–22). A majority of CERs agreed that AI would positively impact CE, shorten CE reading time, and help standardize reporting in CE and characterize lesions seen in CE. Nevertheless, in the foreseeable future, a majority of CERs disagreed with the complete replacement all CE reading by AI. Most CERs believed in the high potential of AI for becoming a valuable tool for automated diagnosis and for shortening the reading time. Currently, the perception is that AI will not replace CE reading. [ABSTRACT FROM AUTHOR]