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首页> 外文期刊>Scandinavian journal of gastroenterology. >Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images
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Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images

机译：卷积神经网络在内窥镜图像基础上评估幽门螺杆菌感染状态的应用

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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.
机译：背景和目的：我们最近报道了人工智能在内窥镜图像的基础上诊断了人工智能在幽门螺杆菌（H.幽门螺杆菌）胃炎的诊断。然而，该研究仅包括H.幽门阳性和阴茎患者，除了幽门螺杆菌后不包括患者。在这项研究中，我们构建了一种卷积神经网络（CNN）并评估其确定所有H.幽门螺杆菌感染状态的能力。方法：在5236名患者的98,564个内窥镜图像的数据集上预先训练和微调。 742 H.幽门阳性，3649-449和845-体化）。通过CNN评估单独的测试数据集（来自847名患者的23,699次图像; 70阳性，493个阴性和284个消除）：培训的CNN输出0和1之间的连续数量作为H.幽门螺杆菌感染的概率指数每个图像状态（pp，h. pylori阳性; pn，负; pe，根除）。三种传染性状态的最可能（最大数量）被选为CNN诊断。在23,699个图像中，CNN被诊断为阳性，23,034的图像，为负，247像根除一样。由于H.幽门螺旋螺旋螺旋螺旋螺旋螺旋螺旋杆菌阴性结果，人工地重新定义为PN-0.9，之后，80％（465/582）的阴性诊断是准确的，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在内窥镜图像的基础上诊断H.幽门螺杆菌感染状态的CNN .Bbraviations：h。幽门螺杆菌：幽门螺杆菌; CNN：卷积神经网络;艾：人工智能; EGD：食管胃部。

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