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Abstract：
UNASSIGNED: Prompt diagnosis of early gastric cancer (EGC) is crucial for improving patient survival. However, most previous computer-aided-diagnosis (CAD) systems did not concretize or explain diagnostic theories. We aimed to develop a logical anthropomorphic artificial intelligence (AI) diagnostic system named ENDOANGEL-LA (logical anthropomorphic) for EGCs under magnifying image enhanced endoscopy (M-IEE).
UNASSIGNED: We retrospectively collected data for 692 patients and 1897 images from Renmin Hospital of Wuhan University, Wuhan, China between Nov 15, 2016 and May 7, 2019. The images were randomly assigned to the training set and test set by patient with a ratio of about 4:1. ENDOANGEL-LA was developed based on feature extraction combining quantitative analysis, deep learning (DL), and machine learning (ML). 11 diagnostic feature indexes were integrated into seven ML models, and an optimal model was selected. The performance of ENDOANGEL-LA was evaluated and compared with endoscopists and sole DL models. The satisfaction of endoscopists on ENDOANGEL-LA and sole DL model was also compared.
UNASSIGNED: Random forest showed the best performance, and demarcation line and microstructures density were the most important feature indexes. The accuracy of ENDOANGEL-LA in images (88.76%) was significantly higher than that of sole DL model (82.77%, p = 0.034) and the novices (71.63%, p<0.001), and comparable to that of the experts (88.95%). The accuracy of ENDOANGEL-LA in videos (87.00%) was significantly higher than that of the sole DL model (68.00%, p<0.001), and comparable to that of the endoscopists (89.00%). The accuracy (87.45%, p<0.001) of novices with the assistance of ENDOANGEL-LA was significantly improved. The satisfaction of endoscopists on ENDOANGEL-LA was significantly higher than that of sole DL model.
UNASSIGNED: We established a logical anthropomorphic system (ENDOANGEL-LA) that can diagnose EGC under M-IEE with diagnostic theory concretization, high accuracy, and good explainability. It has the potential to increase interactivity between endoscopists and CADs, and improve trust and acceptability of endoscopists for CADs.
UNASSIGNED: This work was partly supported by a grant from the Hubei Province Major Science and Technology Innovation Project (2018-916-000-008) and the Fundamental Research Funds for the Central Universities (2042021kf0084).
UNASSIGNED: We retrospectively collected data for 692 patients and 1897 images from Renmin Hospital of Wuhan University, Wuhan, China between Nov 15, 2016 and May 7, 2019. The images were randomly assigned to the training set and test set by patient with a ratio of about 4:1. ENDOANGEL-LA was developed based on feature extraction combining quantitative analysis, deep learning (DL), and machine learning (ML). 11 diagnostic feature indexes were integrated into seven ML models, and an optimal model was selected. The performance of ENDOANGEL-LA was evaluated and compared with endoscopists and sole DL models. The satisfaction of endoscopists on ENDOANGEL-LA and sole DL model was also compared.
UNASSIGNED: Random forest showed the best performance, and demarcation line and microstructures density were the most important feature indexes. The accuracy of ENDOANGEL-LA in images (88.76%) was significantly higher than that of sole DL model (82.77%, p = 0.034) and the novices (71.63%, p<0.001), and comparable to that of the experts (88.95%). The accuracy of ENDOANGEL-LA in videos (87.00%) was significantly higher than that of the sole DL model (68.00%, p<0.001), and comparable to that of the endoscopists (89.00%). The accuracy (87.45%, p<0.001) of novices with the assistance of ENDOANGEL-LA was significantly improved. The satisfaction of endoscopists on ENDOANGEL-LA was significantly higher than that of sole DL model.
UNASSIGNED: We established a logical anthropomorphic system (ENDOANGEL-LA) that can diagnose EGC under M-IEE with diagnostic theory concretization, high accuracy, and good explainability. It has the potential to increase interactivity between endoscopists and CADs, and improve trust and acceptability of endoscopists for CADs.
UNASSIGNED: This work was partly supported by a grant from the Hubei Province Major Science and Technology Innovation Project (2018-916-000-008) and the Fundamental Research Funds for the Central Universities (2042021kf0084).
摘要：
未经证实：早期胃癌（EGC）的迅速诊断对于提高患者生存率至关重要。然而,以前的大多数计算机辅助诊断（CAD）系统都没有具体化或解释诊断理论。我们旨在在放大图像增强内窥镜检查（M-IEE）下为EGC开发一种名为ENDOANGEL-LA（逻辑拟人化）的逻辑拟人化人工智能（AI）诊断系统。
UASSIGNED：我们回顾性地收集了武汉大学人民医院的692名患者和1897张照片，武汉,2016年11月15日至2019年5月7日之间的中国。将图像随机分配给患者的训练集和测试集,比率约为4:1。ENDOANGEL-LA是基于特征提取结合定量分析开发的，深度学习(DL)，机器学习（ML）将11个诊断特征指标集成到7个ML模型中，并选择了最优模型。评估了ENDOANGEL-LA的性能，并与内窥镜医师和唯一的DL模型进行了比较。还比较了内窥镜医师对ENDOANGEL-LA和唯一DL模型的满意度。
UNASSIGNED：随机森林表现出最佳性能，分界线和微结构密度是最重要的特征指标。图像中ENDOANGEL-LA的准确性（88.76％）明显高于单独的DL模型（82.77％，p=0.034)和新手(71.63%，p<0.001)，与专家(88.95%)相当。视频中ENDOANGEL-LA的准确率（87.00%）明显高于单机DL模型（68.00%，p<0.001)，与内窥镜医师（89.00％）相当。精度(87.45%，p<0.001)的新手在ENDOANGEL-LA的协助下显著提高。内窥镜医师对ENDOANGEL-LA的满意度明显高于单独的DL模型。
UNASSIGNED：我们建立了一个逻辑拟人系统（ENDOANGEL-LA），可以在M-IEE下通过诊断理论具体化来诊断EGC，精度高,和良好的解释能力。它有可能增加内窥镜医师和CADs之间的互动，提高内窥镜医师对CADs的信任度和可接受性。
UNASSIGNED：这项工作得到了湖北省重大科技创新项目（2018-916-000-008）和中央大学基础研究基金（2042021kf0084）的部分资助。
UASSIGNED：我们回顾性地收集了武汉大学人民医院的692名患者和1897张照片，武汉,2016年11月15日至2019年5月7日之间的中国。将图像随机分配给患者的训练集和测试集,比率约为4:1。ENDOANGEL-LA是基于特征提取结合定量分析开发的，深度学习(DL)，机器学习（ML）将11个诊断特征指标集成到7个ML模型中，并选择了最优模型。评估了ENDOANGEL-LA的性能，并与内窥镜医师和唯一的DL模型进行了比较。还比较了内窥镜医师对ENDOANGEL-LA和唯一DL模型的满意度。
UNASSIGNED：随机森林表现出最佳性能，分界线和微结构密度是最重要的特征指标。图像中ENDOANGEL-LA的准确性（88.76％）明显高于单独的DL模型（82.77％，p=0.034)和新手(71.63%，p<0.001)，与专家(88.95%)相当。视频中ENDOANGEL-LA的准确率（87.00%）明显高于单机DL模型（68.00%，p<0.001)，与内窥镜医师（89.00％）相当。精度(87.45%，p<0.001)的新手在ENDOANGEL-LA的协助下显著提高。内窥镜医师对ENDOANGEL-LA的满意度明显高于单独的DL模型。
UNASSIGNED：我们建立了一个逻辑拟人系统（ENDOANGEL-LA），可以在M-IEE下通过诊断理论具体化来诊断EGC，精度高,和良好的解释能力。它有可能增加内窥镜医师和CADs之间的互动，提高内窥镜医师对CADs的信任度和可接受性。
UNASSIGNED：这项工作得到了湖北省重大科技创新项目（2018-916-000-008）和中央大学基础研究基金（2042021kf0084）的部分资助。
