Background and aims: Endoscopists' competence can vary widely, as shown in the variation in adenoma detection rate (ADR). Computer-aided quality assessment (CAQ) can automatically assess performance during individual procedures. This review aims to identify and describe different CAQ systems for colonoscopy.
Methods: A systematic review of the literature was done using MEDLINE, EMBASE, and SCOPUS based on three blocks of terms according to the inclusion criteria: Colonoscopy, Competence assessment, and Automatic evaluation. Articles were systematically reviewed by two reviewers, first by abstract and then in full text. The methodological quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI).
Results: 12,575 studies were identified, 6,831 remained after removal of duplicates, and 6,806 did not pass the eligibility criteria and were excluded, leaving thirteen studies for final analysis. Five categories of CAQ systems were identified: Withdrawal speedometer (seven studies), Scope movement analysis (three studies), Effective withdrawal time (one study), Fold examination quality (one study), and Visual gaze pattern (one study). The withdrawal speedometer was the only CAQ system that tested its feedback by examining changes in ADR. Three studies observed an improvement in ADR, and two studies did not. The methodological quality of the studies was high (mean MERSQI 15.2 points, maximum 18 points).
Conclusions: Thirteen studies developed or tested CAQ systems, most frequently by correlating it to ADR. Only five studies tested feedback by implementing the CAQ system. A meta-analysis was impossible due to the heterogeneous study designs, and more studies are warranted.
Keywords: Adenoma Detection Rate; Artificial Intelligence; Assessment; Colonoscopy; Competency; Machine Learning.
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