Computer-aided diagnosis for the resect-and-discard strategy for colorectal polyps: a systematic review and meta-analysis.

BACKGROUND: The resect-and-discard strategy allows endoscopists to replace post-polypectomy pathology with real-time prediction of polyp histology during colonoscopy (optical diagnosis). We aimed to investigate the benefits and harms of implementing computer-aided diagnosis (CADx) for polyp pathology into the resect-and-discard strategy. METHODS: In this systematic review and meta-analysis, we searched MEDLINE, Embase, and Scopus from database inception to June 5, 2024, without language restrictions, for diagnostic accuracy studies that assessed the performance of real-time CADx systems, compared with histology, for the optical diagnosis of diminutive polyps (≤5 mm) in the entire colon. We synthesised data for three strategies: CADx-alone, CADx-unassisted, and CADx-assisted; when the endoscopist was involved in the optical diagnosis, we synthesised data exclusively from diagnoses for which confidence in the prediction was reported as high. The primary outcomes were the proportion of polyps that would have avoided pathological assessment (ie, the proportion optically diagnosed with high confidence; main benefit) and the proportion of polyps incorrectly predicted due to false positives and false negatives (main harm), directly compared between CADx-assisted and CADx-unassisted strategies. We used DerSimonian and Laird's random-effects model to calculate all outcomes. We used Higgins I2 to assess heterogeneity, the Grading of Recommendations, Assessment, Development, and Evaluation approach to rate certainty, and funnel plots and Egger's test to examine publication bias. This study is registered with PROSPERO, CRD42024508440. FINDINGS: We found 1019 studies, of which 11 (7400 diminutive polyps, 3769 patients, and 185 endoscopists) were included in the final meta-analysis. Three studies (1817 patients and 4086 polyps [2148 neoplastic and 1938 non-neoplastic]) provided data to directly compare the primary outcome measures between the CADx-unassisted and CADx-assisted strategies. We found no significant difference between the CADx-assisted and CADx-unassisted strategies for the proportion of polyps that would have avoided pathological assessment (90% [88-93], 3653 [89·4%] of 4086 polyps diagnosed with high confidence vs 90% [95% CI 85-94], 3588 [87·8%] of 4086 polyps diagnosed with high confidence; risk ratio 1·01 [95% CI 0·99-1·04; I2=53·49%; low-certainty evidence; Egger's test p=0·18). The proportion of incorrectly predicted polyps was lower with the CADx-assisted strategy than with the CADx-unassisted strategy (12% [95% CI 7-17], 523 [14·3%] of 3653 polyps incorrectly predicted with a CADx-assisted strategy vs 13% [6-20], 582 [16·2%] of 3588 polyps incorrectly diagnosed with a CADx-unassisted strategy; risk ratio 0·88 [95% CI 0·79-0·98]; I2=0·00%; low-certainty evidence; Egger's test p=0·18). INTERPRETATION: CADx did not produce benefit nor harm for the resect-and-discard strategy, questioning its value in clinical practice. Improving the accuracy and explainability of CADx is desired. FUNDING: European Commission (Horizon Europe), the Japan Society of Promotion of Science, and Associazione Italiana per la Ricerca sul Cancro.

Adenoma detection rate in colonoscopy: how can it be improved?

INTRODUCTION: The introduction of widespread colonoscopy screening programs has helped in decreasing the incidence of Colorectal Cancer (CRC). However, 'back-to-back' colonoscopies revealed relevant percentage of missed adenomas. Quality indicators were created to further homogenize detection performances and decrease the incidence of post-colonoscopy CRC. Among them, the Adenoma Detection Rate (ADR), defined as the percentage obtained by dividing the number of endoscopic procedures in which at least one adenoma was resected, by the total number of procedures, was found to be inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer. AREAS COVERED: In this paper, we performed a comprehensive review of the literature focusing on promising new devices and technologies, which are meant to positively affect the endoscopist performance in detecting adenomas, therefore increasing ADR. EXPERT OPINION: Considering the current knowledge, although several devices and technologies have been proposed with this intent, the recent implementation of AI ranked over all of the other strategies and it is likely to become the new standard within few years. However, the combination of different device/technologies need to be investigated in the future aiming at even further increasing of endoscopist detection performances.

Evaluating the potential of artificial intelligence in ulcerative colitis.

INTRODUCTION: Diagnosis and therapeutic management in ulcerative colitis (UC) relies on a combination of endoscopic and histological scorings which are difficult to objectively quantify. Artificial intelligence (AI) might overcome the current issues of inter-observer variability, repetitive need for biopsies and estimation of disease activity medicine currently encourages. AREAS COVERED: With this narrative literature review we aim to provide a clear and critical overview of the recent evolutions in the field of AI and UC, based on a literature search performed on Pubmed, Embase and Cochrane Library. The major focus of this review is the use of AI for endoscopic assessment of disease activity and the correlation with histology and long-term outcome. Moreover, we elucidate on the more recent developments in the field of AI as support in histological disease assessment, surveillance, therapy monitoring and natural language processing. EXPERT OPINION: UC management is evolving with AI impacting nearly every aspect of it. The immediate future influence of AI in UC management will be focussed on the collection, extraction and organization of particular clinical information. Expect is the transformation toward a real-time standardized, reproducible, objective and high-reliable disease grading, especially in endoscopy, histology and eventually radiology applications for UC.