Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China.
PLoS One. 2024 May 14;19(5):e0303421. doi: 10.1371/journal.pone.0303421. eCollection 2024.
Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance.
The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data.
Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%).
AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.
胃肠上皮化生是一种癌前病变，及时诊断对于延缓或阻止癌症进展至关重要。人工智能（AI）在疾病诊断领域得到了广泛应用。本研究旨在全面评估 AI 诊断内镜下胃肠上皮化生的准确性，比较其与内镜医师的能力，并探讨影响 AI 性能的主要因素。
本研究遵循 PRISMA-DTA 指南，检索了 PubMed、Embase、Web of Science、Cochrane 和 IEEE Xplore 数据库，纳入截至 2023 年 10 月发表的相关研究。我们提取了每个研究的关键特征和实验数据，并通过荟萃分析合并了敏感性和特异性指标。然后，我们使用相同的测试数据比较了 AI 与内镜医师的诊断能力。
共纳入 12 项研究，包含 11173 例患者，结果表明 AI 模型在诊断胃肠上皮化生方面具有良好的效果。荟萃分析得出的合并敏感性为 94%（95%置信区间：0.92-0.96），特异性为 93%（95%置信区间：0.89-0.95）。综合受试者工作特征曲线下面积为 0.97。荟萃回归和亚组分析的结果表明，研究设计、内镜类型、训练图像数量和算法等因素对 AI 的诊断性能有显著影响。AI 的诊断能力优于内镜医师（敏感性：95%比 79%）。
基于内镜的 AI 辅助诊断胃肠上皮化生具有较高的性能和临床诊断价值。然而，还需要进一步的前瞻性研究来验证这些发现。
