Background and Aims

Artificial intelligence (AI)-assisted detection is increasingly used in upper endoscopy. We performed a meta-analysis to determine the diagnostic accuracy of AI on detection of gastric and esophageal neoplastic lesions and Helicobacter pylori (HP) status.

Methods

We searched Embase, PubMed, Medline, Web of Science, and Cochrane databases for studies on AI detection of gastric or esophageal neoplastic lesions and HP status. After assessing study quality using the Quality Assessment of Diagnostic Accuracy Studies tool, a bivariate meta-analysis following a random-effects model was used to summarize the data and plot hierarchical summary receiver-operating characteristic curves. The diagnostic accuracy was determined by the area under the hierarchical summary receiver-operating characteristic curve (AUC).

Results

Twenty-three studies including 969,318 images were included. The AUC of AI detection of neoplastic lesions in the stomach, Barrett’s esophagus, and squamous esophagus and HP status were .96 (95% confidence interval [CI], .94-.99), .96 (95% CI, .93-.99), .88 (95% CI, .82-.96), and .92 (95% CI, .88-.97), respectively. AI using narrow-band imaging was superior to white-light imaging on detection of neoplastic lesions in squamous esophagus (.92 vs .83, P < .001). The performance of AI was superior to endoscopists in the detection of neoplastic lesions in the stomach (AUC, .98 vs .87; P < .001), Barrett’s esophagus (AUC, .96 vs .82; P < .001), and HP status (AUC, .90 vs .82; P < .001).

Conclusions

AI is accurate in the detection of upper GI neoplastic lesions and HP infection status. However, most studies were based on retrospective reviews of selected images, which requires further validation in prospective trials.

中文翻译：

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人工智能辅助检测上消化道病变的准确性：系统评价和荟萃分析。

背景和目标

人工智能（AI）辅助检测越来越多地用于上内窥镜检查中。我们进行了一项荟萃分析，以确定AI在检测胃和食道肿瘤病变和幽门螺杆菌（HP）状态方面的诊断准确性。

方法

我们搜索了Embase，PubMed，Medline，Web of Science和Cochrane数据库，以进行AI检测胃或食道肿瘤性病变和HP状态的研究。在使用“诊断准确性研究质量评估”工具评估研究质量之后，使用遵循随机效应模型的双变量荟萃分析来汇总数据并绘制分层汇总的接收者操作特征曲线。诊断准确性由分层汇总接收器工作特性曲线（AUC）下的面积确定。

结果

二十三项研究包括969,318张图像。AI检测到胃，巴雷特食管和鳞状食道的肿瘤性病变的AUC的AUC为.96（95％置信区间[CI] ，. 94-.99）、. 96（95％CI，.93- .99）、. 88（95％CI，.82-.96）和.92（95％CI，.88-.97）。在检测鳞状食道的肿瘤病变方面，使用窄带成像的AI优于白光成像（.92 vs.83，P  <.001）。在胃肿瘤性病变（AUC，.98 vs .87；P  <.001），Barrett食管（AUC，.96 vs .82；P  <.001）和AI方面，AI的性能优于内镜医师。HP状态（AUC，.90和.82；P  <.001）。

结论

AI在检测上消化道肿瘤病变和HP感染状态方面是准确的。但是，大多数研究都是基于对选定图像的回顾性审查，这需要在前瞻性试验中进行进一步验证。