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

作者：Jing Wang, Ying Li, Shuyu Li, Honggang Yu, Boru Chen, Cheng Du, Fei Liao, Tao Tan, Qinghong Xu, Zhifeng Liu, Yuan Huang, Ci Zhu, Wenbing Cao, Liwen Yao, Zhifeng Wu, Lianlian Wu, Chenxia Zhang, Bing Xiao, Ming Xu, Jun Li · 发表于：Endoscopy · 年份：2023 · DOI：10.1055/a-2189-7036 · 被引用次数：35 · 研究领域：Colorectal Cancer Screening and Detection、Colorectal Cancer Surgical Treatments、Surgical Simulation and Training

Abstract Background The choice of polypectomy device and surveillance intervals for 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. Methods ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. Results The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P<0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P<0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than...