Artificial intelligence assisted standard white light endoscopy accurately characters early colorectal cancer: a multicenter diagnostic study
Meng, S.; Zheng, Y.; Su, R.; Wang, W.; Zhang, Y.; Xiao, H.; Han, Z.; Zhang, W.; Qin, W.; Yang, C.; Yan, L.; Xu, H.; Bu, Y.; Zhong, Y.; Zhang, Y.; He, Y.; Qiu, H.; Xu, W.; Chen, H.; Wu, S.; Jiang, Z.; Zhang, Y.; Dong, C.; Hu, Y.; Xie, L.; Li, X.; Jiang, J.; Zhu, H.; Li, W.; Wen, Z.; Zheng, X.; Sun, Y.; Zhou, X.; Ding, L.; Zhang, C.; Pan, W.; Wu, S.; Hu, Y.
Show abstract
Colorectal cancer (CRC) is the third in incidence and mortality1 of cancer. Screening with colonoscopy has been shown to reduce mortality by 40-60%2. Challenge for screening indistinguishable precancerous and noninvasive lesion using conventional colonoscopy was still existing3. We propose to establish a propagable artificial intelligence assisted high malignant potential early CRC characterization system (ECRC-CAD). 4,390 endoscopic images of early CRC were used to establish the model. The diagnostic accuracy of high malignant potential early CRC was 0.963 (95% CI, 0.941-0.978) in the internal validation set and 0.835 (95% CI, 0.805-0.862) in external datasets. It achieved better performance than the expert endoscopists. Spreading of ECRC-CAD to regions with different medical levels can assist in CRC screening and prevention.
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