P489 Systematic review and meta-analysis of the accuracy of computer-aided diagnosis in identifying endoscopic remission in Ulcerative Colitis

Abstract

Abstract Background Endoscopic remission (ER), the absence of active inflammation during endoscopy, is a key therapeutic target in Ulcerative Colitis (UC). ER is assessed during colonoscopy using standardised scoring systems. However, there remains significant inter-operator variability in assessing ER, for which the application of computer-aided diagnosis (CADx) could be a potential solution. A previous systematic review and meta-analysis on this topic was limited to literature up to June 2022 and did not assess the methodological quality of studies or heterogeneity amongst results. We aimed to update this review. Methods Searches were performed on six electronic databases and relevant reference lists up to May 2023. Data was extracted and screened independently by two reviewers. Methodological assessment was performed using the QUADAS-2 tool. An ‘image selection’ domain was added to the tool given the importance of endoscopic image quality in training and testing data on CADx’s accuracy. Meta-analysis was performed using a bivariate model. Heterogeneity was assessed through visual inspection of forest plots and receiver-operating characteristic curves, subgroup analyses and meta-regression. The overall quality of evidence was assessed using GRADE methodology. Results 17 studies (16 full-text articles and one abstract) were identified. 15 studies adopted a cross-sectional design. All studies used expert endoscopists scoring of ER as the reference standard. The Mayo Endoscopic Score was the most commonly used scoring system (13/17). CADx assessed ER in real-time in one study, but was applied to images and videos obtained from previous endoscopic procedures in all other studies. Inception-v3 was the most common CADx system used (6/17). ‘Patient selection’ was the domain of greatest concern due to a considerable number of studies using images from the same public database and under-reporting participant characteristics. All studies had a low risk of bias (ROB) in the ‘image selection’ domain. One study was deemed overall high ROB, and two were deemed overall unclear ROB. Sensitivity analysis was performed by removing these studies and the abstract, and pooled estimates were obtained from the remaining 13 studies. CADx had a pooled sensitivity of 90.3% and specificity of 94.6% for diagnosing ER. The overall quality of evidence was moderate due to the failure of a significant proportion of studies to report sufficient participant characteristics. Conclusion There is moderate quality evidence that CADx has high sensitivity and specificity for diagnosing ER in pre-clinical studies. Further evaluation is required in randomised controlled trials to evaluate its efficacy in real-time and human-computer interactions.