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Objectives: To describe patient and public involvement (PPI) in randomized controlled trials (RCTs) addressing a chronic condition and to analyze whether PPI is associated with trial characteristics., Study Design and Setting: We used PubMed search to identify RCTs addressing a chronic condition and published in journals with a mandatory PPI statement., Results: Across 101 RCTs; 40 (40%) reported PPI at any stage of the research process. PPI contribution occurred mostly at the design stage of RCTs (n = 36), especially for assessing the burden of the intervention (n = 24), and at the conduct stage (n = 21), with the elaboration of communication materials (n = 14). Less than one-third (13/40) of RCTs included PPI in the development or choice of outcome measures. As compared with non-PPI RCTs, PPI RCTs more frequently were published in The BMJ, had a corresponding author from the United Kingdom, reported a public funding source, had a higher inclusion rate, used usual care as a control and evaluated a digital intervention. PPI RCTs were associated with less frequent use of placebo as a control group., Conclusion: Our results underline that PPI is not uncommon in RCTs of chronic conditions but infrequently occurred at a key stage. Yet, the engagement of patients as a real partner in RCTs of chronic conditions should be enhanced., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background and Aims: Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps., Methods: We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate data sets of images containing the four different landmarks., Results: After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.7%). The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.4% (95% CI 84.1% to 88.5%), and 86.4% (95% CI 84.1% to 88.6%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.6% (95% CI 86.6% to 90.3%)., Conclusion: This model offers a novel tool to assist endoscopists with automated identification of AO and ICV during colonoscopy. The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality., Competing Interests: MT, MT, MB, ED, SB, and SS do not have any conflicts of interest relevant to this paper to disclose. AB is a co-awardee in funded research projects with Imagia Canexia Health Inc. and a consultant for Medtronic Inc. and A.I. VALI Inc. DvR is supported by a “Fonds de Recherche du Québec Santé” career development award. He has also received research funding from ERBE, Ventage, Pendopharm, and Pentax, and is a consultant for Boston Scientific and Pendopharm. SHG, EJ, TB, SG, and LdJ are full-time and direct employees of the Imagia Canexia Health Inc. at 6650 Saint-Urbain Street, Suite 100, Montréal, Québec H2S 3G9, CANADA., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Canadian Association of Gastroenterology.)