4.6 Article

Identifying key mechanisms leading to visual recognition errors for missed colorectal polyps using eye-tracking technology

Journal

Publisher

WILEY
DOI: 10.1111/jgh.16127

Keywords

artificial intelligence; colonic polyps; colonoscopy; colorectal cancer

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This study aimed to evaluate visual recognition errors in colorectal polyps and explore the underlying mechanisms. The findings showed that cognitive errors were the main reason for visual recognition errors, while gaze errors were less common. Artificial intelligence performed better in identifying colorectal polyps compared to trainees and medical students.
Background and AimLack of visual recognition of colorectal polyps may lead to interval cancers. The mechanisms contributing to perceptual variation, particularly for subtle and advanced colorectal neoplasia, have scarcely been investigated. We aimed to evaluate visual recognition errors and provide novel mechanistic insights. MethodsEleven participants (seven trainees and four medical students) evaluated images from the UCL polyp perception dataset, containing 25 polyps, using eye-tracking equipment. Gaze errors were defined as those where the lesion was not observed according to eye-tracking technology. Cognitive errors occurred when lesions were observed but not recognized as polyps by participants. A video study was also performed including 39 subtle polyps, where polyp recognition performance was compared with a convolutional neural network. ResultsCognitive errors occurred more frequently than gaze errors overall (65.6%), with a significantly higher proportion in trainees (P = 0.0264). In the video validation, the convolutional neural network detected significantly more polyps than trainees and medical students, with per-polyp sensitivities of 79.5%, 30.0%, and 15.4%, respectively. ConclusionsCognitive errors were the most common reason for visual recognition errors. The impact of interventions such as artificial intelligence, particularly on different types of perceptual errors, needs further investigation including potential effects on learning curves. To facilitate future research, a publicly accessible visual perception colonoscopy polyp database was created.

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