Impact of Artificial Intelligence on Polyp Size and Surveillance Colonoscopy: A Phantom Study

Background Artificial intelligence (AI) is a hot topic in the world of medicine. AI may be useful in identifying and sizing polyps, which influence surveillance intervals. Therefore, we examined polyp size estimation by AI using a survey study. Methods A survey study was performed using a phantom colon model. Eleven videos were produced in the colon phantom using a colonoscope. Gastroenterologists were compared to a new AI system (Argus) for sizing polyps and their impact on surveillance intervals. Results Eleven gastroenterologists completed the survey with a mean age of 51.1 ± 8.1 years and an average of 19.3 ± 10 years of experience. Mean accuracy rates for gastroenterologists were 76% ± 0.1% (range 54-89%) compared to 96% ± 0.05% for Argus. Endoscopists estimated polyp size within ± 1 mm 44 times (36%) versus 9 times (82%) with Argus. Endoscopists' surveillance recommendations were significantly more often inappropriate compared to Argus (34 vs 0). The interval of next colonoscopy was too short for 27 endoscopists (22%) and too long for seven endoscopists (6%). Conclusions AI appears to be more accurate in estimating polyp size than experienced endoscopists. Given the potential impact on surveillance intervals, AI may result in cost savings.