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Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images

机译：基于内窥镜图像的卷积神经网络在幽门螺杆菌感染诊断中的应用

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Background and aims The role of artificial intelligence in the diagnosis of Helicobacter pylori gastritis based on endoscopic images has not been evaluated. We constructed a convolutional neural network (CNN), and evaluated its ability to diagnose H. pylori infection. Methods A 22-layer, deep CNN was pre-trained and fine-tuned on a dataset of 32,208 images either positive or negative for H. pylori (first CNN). Another CNN was trained using images classified according to 8 anatomical locations (secondary CNN). A separate test data set (11,481 images from 397 patients) was evaluated by the CNN, and 23 endoscopists, independently. Results The sensitivity, specificity, accuracy, and diagnostic time were 81.9%, 83.4%, 83.1%, and 198 s, respectively, for the first CNN, and 88.9%, 87.4%, 87.7%, and 194 s, respectively, for the secondary CNN. These values for the 23 endoscopists were 79.0%, 83.2%, 82.4%, and 230 ± 65 min (85.2%, 89.3%, 88.6%, and 253 ± 92 min by 6 board-certified endoscopists), respectively. The secondary CNN had a significantly higher accuracy than endoscopists (by 5.3%; 95% CI, 0.3–10.2). Conclusion H. pylori gastritis could be diagnosed based on endoscopic images using CNN with higher accuracy and in a considerably shorter time compared to manual diagnosis by endoscopists.
机译：背景和目的尚未评估人工智能在基于内窥镜图像诊断幽门螺杆菌胃炎中的作用。我们构建了卷积神经网络（CNN），并评估了其诊断幽门螺杆菌感染的能力。方法对22层深的CNN进行预训练，并在32208张对幽门螺杆菌阳性或阴性的图像（第一个CNN）上进行微调。使用根据8个解剖位置分类的图像（辅助CNN）训练了另一个CNN。 CNN和23位内镜医师分别评估了一个单独的测试数据集（来自397例患者的11481张图像）。结果对于第一个CNN，敏感性，特异性，准确性和诊断时间分别为81.9％，83.4％，83.1％和198 s，对于第一个CNN分别为88.9％，87.4％，87.7％和194 s。次要CNN。 23位内镜医师的这些值分别为79.0％，83.2％，82.4％和230±65分钟（6位经董事会认证的内镜医师的85.2％，89.3％，88.6％和253±92分钟）。次要CNN的准确性比内镜医师高得多（提高了5.3％; 95％CI为0.3-10.2）。结论与内窥镜检查者的手动诊断相比，使用CNN可以根据内窥镜图像诊断幽门螺杆菌胃炎的准确性更高，且时间要短得多。

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