Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images
Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images
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DOI：
10.1080/00365521.2019.1577486
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发表时间：
2019-02-01
影响因子：
1.9
通讯作者：
Tada, Tomohiro
中科院分区：
文献类型：
--
作者：
Shichijo, Satoki;Endo, Yuma;Tada, Tomohiro
背景和目的：我们最近报道了人工智能在幽门螺杆菌（H。pylori）胃炎。然而，该研究仅包括H。pylori阳性和阴性患者，排除H.根除幽门在这项研究中，我们构建了一个卷积神经网络（CNN），并评估了它确定所有H的能力。方法：在来自5236名患者（742例幽门螺杆菌感染）的98，564个内窥镜图像的数据集上预训练和微调深度CNN。pylori阳性，3649例阴性，845例根除）。CNN评估了一个单独的测试数据集（来自847名患者的23，699张图像; 70张阳性，493张阴性，284张根除）。pylori感染状况（Pp，H. pylori阳性; Pn阴性; Pe根除）。选择三种感染状态中最可能的（最大数量）作为CNN诊断。在23,699张图片中，CNN诊断出418张图片为阳性，23,034张为阴性，247张为根除。由于H. pylori阴性者，H.将pylori阴性重新定义为Pn-0.9，此后80%（465/582）的阴性诊断准确，84%（147/174）根除，48%（44/91）阳性。诊断23，699张图像所需的时间为261秒。结论：我们使用了一种新的算法来构建用于诊断H.幽门螺杆菌感染状况的基础上，内镜图像非常迅速。pylori：幽门螺杆菌; CNN：卷积神经网络; AI：人工智能; EGD：食管胃镜检查。
Background and aim: We recently reported the role of artificial intelligence in the diagnosis of Helicobacter pylori (H. pylori) gastritis on the basis of endoscopic images. However, that study included only H. pylori-positive and -negative patients, excluding patients after H. pylori-eradication. In this study, we constructed a convolutional neural network (CNN) and evaluated its ability to ascertain all H. pylori infection statuses.Methods: A deep CNN was pre-trained and fine-tuned on a dataset of 98,564 endoscopic images from 5236 patients (742 H. pylori-positive, 3649 -negative, and 845 -eradicated). A separate test data set (23,699 images from 847 patients; 70 positive, 493 negative, and 284 eradicated) was evaluated by the CNN.Results: The trained CNN outputs a continuous number between 0 and 1 as the probability index for H. pylori infection status per image (Pp, H. pylori-positive; Pn, negative; Pe, eradicated). The most probable (largest number) of the three infectious statuses was selected as the CNN diagnosis'. Among 23,699 images, the CNN diagnosed 418 images as positive, 23,034 as negative, and 247 as eradicated. Because of the large number of H. pylori negative findings, the probability of H. pylori-negative was artificially re-defined as Pn -0.9, after which 80% (465/582) of negative diagnoses were accurate, 84% (147/174) eradicated, and 48% (44/91) positive. The time needed to diagnose 23,699 images was 261seconds.Conclusion: We used a novel algorithm to construct a CNN for diagnosing H. pylori infection status on the basis of endoscopic images very quickly.Abbreviations:H. pylori: Helicobacter pylori; CNN: convolutional neural network; AI: artificial intelligence; EGD: esophagogastroduodenoscopies.