Xiao-Xiao Yang1, Li Zhen1,2, Xue-Jun Shao3, Rui Ji1,4, Jun-Yan Qu1, Meng-Qi Zheng1, Yi-Ning Sun1, Ru-Chen Zhou1, Hang You1, Li-Xiang Li1,2, Jian Feng3, Xiao-Yun Yang1,2, Yan-Qing Li1,2, Xiu-Li Zuo1,4. 1. Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China. 2. Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China. 3. Qingdao Medicon Digital Engineering Co. Ltd. 4. Robot engineering laboratory for precise diagnosis and therapy of GI tumor, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Abstract
BACKGROUND AND AIMS: Endoscopic diagnosis of early esophageal squamous cell cancer (ESCC) is complicated and dependent on operators' experience. This study aimed to develop an artificial intelligence (AI) model for automatic diagnosis of early ESCC. METHODS: Non-magnifying and magnifying endoscopic images of normal/noncancerous lesion, early ESCC and advanced esophageal cancer (AEC) were retrospectively obtained from Qilu Hospital of Shandong University. A total of 10,988 images from 5,075 cases were chosen for training and validation. Another 2,309 images from 1,055 cases were collected for testing. One hundred and four real-time videos were also collected to evaluate the diagnostic performance of the AI model. The diagnostic performance of the AI model was compared with endoscopists by magnifying images and the assistant efficiency of the AI model for novices was evaluated. RESULTS: The AI diagnosis for non-magnifying images showed a per-patient accuracy, sensitivity, and specificity of 99.5%, 100%, 99.5% for white light imaging, and 97.0%, 97.2%, 96.4% for optical enhancement/iodine straining images. Regarding diagnosis for magnifying images, the per-patient accuracy, sensitivity, and specificity was 88.1%, 90.9% and 85.0%. The diagnostic accuracy of the AI model was similar to experts (84.5%, P=0.205) and superior to novices (68.5%, P=0.005). The diagnostic performance of novices was significantly improved by AI assistance. When it comes to the diagnosis for real-time videos, the AI model showed acceptable performance as well. CONCLUSIONS: The AI model could accurately recognize early ESCC among noncancerous mucosa and AEC. It could be a potential assistant for endoscopists, especially for novices. This article is protected by copyright. All rights reserved.
BACKGROUND AND AIMS: Endoscopic diagnosis of early esophageal squamous cell cancer (ESCC) is complicated and dependent on operators' experience. This study aimed to develop an artificial intelligence (AI) model for automatic diagnosis of early ESCC. METHODS: Non-magnifying and magnifying endoscopic images of normal/noncancerous lesion, early ESCC and advanced esophageal cancer (AEC) were retrospectively obtained from Qilu Hospital of Shandong University. A total of 10,988 images from 5,075 cases were chosen for training and validation. Another 2,309 images from 1,055 cases were collected for testing. One hundred and four real-time videos were also collected to evaluate the diagnostic performance of the AI model. The diagnostic performance of the AI model was compared with endoscopists by magnifying images and the assistant efficiency of the AI model for novices was evaluated. RESULTS: The AI diagnosis for non-magnifying images showed a per-patient accuracy, sensitivity, and specificity of 99.5%, 100%, 99.5% for white light imaging, and 97.0%, 97.2%, 96.4% for optical enhancement/iodine straining images. Regarding diagnosis for magnifying images, the per-patient accuracy, sensitivity, and specificity was 88.1%, 90.9% and 85.0%. The diagnostic accuracy of the AI model was similar to experts (84.5%, P=0.205) and superior to novices (68.5%, P=0.005). The diagnostic performance of novices was significantly improved by AI assistance. When it comes to the diagnosis for real-time videos, the AI model showed acceptable performance as well. CONCLUSIONS: The AI model could accurately recognize early ESCC among noncancerous mucosa and AEC. It could be a potential assistant for endoscopists, especially for novices. This article is protected by copyright. All rights reserved.
Authors: Fei Kuang; Juan Du; Mengjia Zhou; Xiangdong Liu; Xinchen Luo; Yong Tang; Bo Li; Song Su Journal: Front Oncol Date: 2022-06-10 Impact factor: 5.738