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To improve colorectal polyp detection, a new artificial intelligence (AI) system (GI-Genius, Medtronic) was trained and validated on a dataset of white-light endoscopy videos from a high-quality randomised controlled trial in comparison to the detection rate and the reaction time (RT) on a lesion basis (n=337/338, sensitivity: 99.7%); false-positive frames were seen in less than 1% of frames from the whole colonoscopy. The RT was faster by AI system as compared with endoscopists in 82% of cases (n=277/337; difference 1.27+3.81 s). This promising system will be tested in clinical studies.
In more detail
Despite its efficacy in colorectal cancer prevention, colonoscopy is affected by a high miss rate of neoplastic lesions and unacceptable variability in adenoma detection rate among individual endoscopists.1 2 By aiding polyp detection on colonoscopy images (figure 1), artificial intelligence (AI) can reduce performance variability. AI algorithms for object detection usually comprise a convolutional neural network trained using as ground truth images annotated by experts. Once trained, these AI systems are able to detect and pinpoint objects in real time, such as colorectal polyps. A new AI system (GI-Genius, Medtronic) was trained and validated using a series of videos of 2684 histologically confirmed polyps from 840 patients who underwent high-definition white-light colonoscopy as part of a previous randomised controlled study with centralised pathology.3 A total of 1.5 million images showing these polyps from different perspectives were extracted from videos and manually annotated by expert endoscopists. For the purpose of the study, patients were randomised between a validation group and a training group. In detail, for the validation phase, 338 polyps (168/338 adenomas or sessile serrated adenomas, 49.7%) from 105 patients were used. For each of these polyps, a video clip was cut starting 5 s before polyp appearance and ending when the snare/biopsy forceps appeared. To assess sensitivity, a true-positive per …
Footnotes
Contributors CH, MBW, PS and AR: substantial contributions to the conception or design of the work, or the acquisition, analysis or interpretation of data; final approval of the version published. All authors: drafting the work or revising it critically for important intellectual content; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests CH, AR and MBW: consultancy for Medtronic.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; internally peer reviewed.