In Journal of gastroenterology and hepatology ; h5-index 51.0
BACKGROUND AND AIMS : Lack of visual recognition of colorectal polyps may lead to interval cancers. The mechanisms contributing to perceptual variation, particularly for subtle and advanced colorectal neoplasia, has scarcely been investigated. We aimed to evaluate visual recognition errors and provide novel mechanistic insights.
METHODS : Eleven participants (7 trainees, 4 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 recognised 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 (CNN).
RESULTS : Cognitive 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 CNN detected significantly more polyps than trainees and medical students, with per polyp sensitivities of 79.5%, 30.0% and 15.4% respectively.
CONCLUSIONS : Cognitive 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.
Ahmad Omer F, Mazomenos Evangelos, Chadebecq Francois, Kader Rawen, Hussein Mohamed, Haidry Rehan J, González-Bueno Puyal Juana, Brandao Patrick, Toth Daniel, Mountney Peter, Seward Ed, Vega Roser, Stoyanov Danail, Lovat Laurence B
2023-Jan-18
Artificial Intelligence, Colonic Polyps, Colonoscopy, Colorectal cancer
