人工智能在胃镜图像中预测幽门螺杆菌感染的应用：诊断试验准确性的系统评价和荟萃分析。
Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.
机构信息
Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.
Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Republic of Korea.
出版信息
J Med Internet Res. 2020 Sep 16;22(9):e21983. doi: 10.2196/21983.
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.
背景
幽门螺杆菌在胃癌的发展中起着核心作用，通过观察胃黏膜对其进行感染预测是内镜的重要功能。然而，目前尚无使用内镜图像对幽门螺杆菌感染进行光学诊断的确立方法。明确诊断需要进行内镜活检。人工智能（AI）已越来越多地应用于临床实践，特别是在图像识别和分类方面。
目的
本研究旨在评估使用内镜图像预测幽门螺杆菌感染的 AI 的诊断测试准确性。
方法
两名独立评估员搜索核心数据库。纳入标准包括具有幽门螺杆菌感染内镜图像且应用 AI 预测幽门螺杆菌感染的表现出诊断性能的研究。进行了系统评价和诊断测试准确性荟萃分析。
结果
最终确定了 8 项研究。AI 预测幽门螺杆菌感染的汇总敏感性、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 15-112）和 0.92（95%CI 0.90-0.94），涉及 1719 名患者（385 名幽门螺杆菌感染患者与 1334 名对照）。元回归显示方法学质量和纳入的每个研究中的患者数量存在异质性。未发现发表偏倚的证据。AI 算法在区分未感染图像和根除后图像方面的准确率达到 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。
试验注册
PROSPERO CRD42020175957；https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957。
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