Background: Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. Objective: This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. Methods: Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. Results: Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. Conclusions: An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. Trial Registration: PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957

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中文翻译：

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人工智能在内窥镜图像中预测幽门螺杆菌感染：诊断测试准确性的系统回顾和荟萃分析。


背景：幽门螺杆菌在胃癌的发生发展中起着核心作用，通过目视检查胃粘膜来预测幽门螺杆菌感染是内窥镜的重要功能。然而，目前还没有使用内窥镜图像对幽门螺杆菌感染进行光学诊断的既定方法。确诊需要内镜活检。人工智能（AI）在临床实践中得到越来越多的采用，特别是在图像识别和分类方面。目的：本研究旨在评估 AI 使用内窥镜图像预测幽门螺杆菌感染的诊断测试准确性。方法：两名独立评估员检索核心数据库。纳入标准包括幽门螺杆菌感染内窥镜图像的研究以及应用人工智能预测幽门螺杆菌感染的诊断性能研究。进行了系统审查和诊断测试准确性荟萃分析。结果：最终确定了 8 项研究。预测幽门螺杆菌感染的 AI 汇总敏感性、特异性、诊断优势比和曲线下面积分别为 0.87 (95% CI 0.72-0.94)、0.86 (95% CI 0.77-0.92)、40 (95% CI 15)在 1719 名患者中（385 名幽门螺杆菌感染患者与 1334 名对照患者）分别为 -112）和 0.92（95% CI 0.90-0.94）。荟萃回归显示了方法学质量，并出于异质性目的纳入了每项研究中的患者人数。没有证据表明存在发表偏倚。 AI算法区分非感染图像和根治后图像的准确率达到82%。结论：AI算法是内镜诊断幽门螺杆菌感染的可靠工具。 应克服缺乏外部验证性能和仅在亚洲进行的局限性。试用注册：PROSPERO CRD42020175957； https://www.crd.york.ac.uk/prospero/display_record.php？记录ID=175957


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