A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation.

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Abstract

Polypectomy devices and surveillance intervals of colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time.ENDOANGEL-CPS calculated polyp size by estimating the distance from endoscopic lens to the polyp and parameters of the lens. The depth estimator network was developed on 7297 images of 5 virtual colon videos and tested on 730 images of 7 virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon attached with polyps, then tested in 157 real-world prospective videos from 3 hospitals, comparing with that of operators. It further compared with nine endoscopists in 69 videos. Inappropriate surveillance recommendations caused by wrong estimation of polyp size were also analyzed.The relative error of depth estimation was 11.31%±6.01% in successive virtual colon images. The concordance correlation coefficients (CCC) between system estimation and ground truth were 0.887 and 0.929 in images of a simulated colon and multicenter videos of 157 polyps. The CCC of ENDOANGEL-CPS surpassed all endoscopists (0.890 vs. 0.412±0.290, p<0.0001). The relative accuracy of ENDOANGEL-CPS is significantly higher than that of endoscopists (89.86% vs. 54.74%, p<0.0001). Regarding inappropriate surveillance recommendations, system’s error rate is also lower than that of endoscopists (1.45% vs. 16.60%, p<0.0001).ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals.Thieme. All rights reserved.