作者先前建立了深度学习模型，使用内镜图像来预测胃部病变的组织病理学和侵袭深度。该研究旨在建立和验证基于深度学习的临床决策支持系统（CDSS），实现胃部肿瘤的自动检测和分类（诊断和侵袭深度预测）的实时内窥镜诊断。使用同样的5,017张内窥镜图像来作为训练数据，这些图像也曾被用于建立以前的模型。主要的结果指标为1. 检测模型的病变检测率和2. 分类模型的病变分类准确率。为了验证检测模型的性能，对2524例实时手术进行了随机的试验。连续的患者要么接受CDSS辅助筛查内窥镜，要么接受常规筛查内窥镜。比较两组的病变检测率。为了验证病变分类模型的性能，进行了一项前瞻性的多中心外部测试，使用了来自五个机构的3,976张新图像。病变检测率为95.6%（内部测试）。在性能验证方面，CDSS辅助的内窥镜检查显示出更高的病变检测率，虽然统计学上不显著（2.0% vs. 1.3%，P-value = 0.21）（随机研究）。在四类分类（晚期和早期胃癌、畸胎瘤和非肿瘤）和浆液膜限制或粘膜下侵袭的侵袭深度预测方面，病变分类率分别为89.7%和89.2%（内部测试）。在性能验证方面，CDSS在四类分类和二进制分类方面的准确率分别为81.5%和86.4%（前瞻性多中心外部测试）。CDSS在检测胃部病变和分类检测方面表现出潜在的临床应用和高性能。Thieme版权所有。

Authors previously established deep-learning models to predict the histopathology and invasion-depth of gastric lesions using endoscopic images. This study aimed to establish and validate a deep-learning-based clinical decision support system (CDSS) for the automated detection and classification (diagnosis and invasion-depth prediction) of gastric neoplasms in real-time endoscopy.The same 5,017 endoscopic images, which were employed to establish previous models, were used for the training data. The primary outcomes were the 1. Lesion-detection rate for the detection model and 2. Lesion-classification accuracy for the classification model. For the performance validation of lesion-detection model, 2,524 real-time procedures were tested in a randomized pilot study. Consecutive patients were allocated either to CDSS-assisted screening endoscopy or conventional screening endoscopy. The lesion-detection rate was compared between the groups. For the performance validation of lesion-classification model, a prospective multicenter external-test was conducted using 3,976 novel images from five institutions.The lesion-detection rate was 95.6% (internal-test). For the performance validation, CDSS-assisted endoscopy showed higher lesion-detection rate compared to conventional screening endoscopy, although statistically not significant (2.0% vs. 1.3%, P-value=0.21) (randomized study). The lesion-classification rate was 89.7% in the four-class classification (advanced-, early gastric cancer, dysplasia, and non-neoplasm) and 89.2% in the invasion-depth prediction (mucosa-confined or submucosa-invaded) (internal-test). For the performance validation, CDSS reached 81.5% accuracy in the four-class classification and 86.4% accuracy in the binary classification (prospective multicenter external-test).The CDSS demonstrated potential for real-clinic application and high performance in terms of lesion detection and classification of detected lesions in the stomach.Thieme. All rights reserved.