PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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%).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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%).
CONCLUSIONS: 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.
摘要：
目的：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的应用。本研究旨在对AI在胃镜检查中检测胃肠上皮化生的诊断准确性进行综合评价,将其与内窥镜医师的能力进行比较，并探讨影响人工智能绩效的主要因素。
方法：研究遵循PRISMA-DTA指南，和PubMed,Embase,WebofScience,科克伦,和IEEEXplore数据库进行了搜索，包括2023年10月发表的相关研究。我们提取了每项研究的关键特征和实验数据，并通过荟萃分析将敏感性和特异性指标结合起来。然后，我们使用相同的测试数据比较了AI与内窥镜医师的诊断能力。
结果：纳入了12项研究，11,173名患者，证明AI模型在诊断胃肠上皮化生中的功效。荟萃分析的合并敏感性为94％（95％置信区间：0.92-0.96），特异性为93％（95％置信区间：0.89-0.95）。接收器工作特征曲线下的组合面积为0.97。荟萃回归和亚组分析结果表明,研究设计等因素,内窥镜类型，训练图像的数量，算法对人工智能的诊断性能有显著影响。AI表现出比内窥镜医师更高的诊断能力(灵敏度：95%vs.79%）。
结论：AI辅助内镜诊断胃肠上皮化生具有较高的表现和临床诊断价值。然而,需要进一步的前瞻性研究来验证这些发现.
方法：研究遵循PRISMA-DTA指南，和PubMed,Embase,WebofScience,科克伦,和IEEEXplore数据库进行了搜索，包括2023年10月发表的相关研究。我们提取了每项研究的关键特征和实验数据，并通过荟萃分析将敏感性和特异性指标结合起来。然后，我们使用相同的测试数据比较了AI与内窥镜医师的诊断能力。
结果：纳入了12项研究，11,173名患者，证明AI模型在诊断胃肠上皮化生中的功效。荟萃分析的合并敏感性为94％（95％置信区间：0.92-0.96），特异性为93％（95％置信区间：0.89-0.95）。接收器工作特征曲线下的组合面积为0.97。荟萃回归和亚组分析结果表明,研究设计等因素,内窥镜类型，训练图像的数量，算法对人工智能的诊断性能有显著影响。AI表现出比内窥镜医师更高的诊断能力(灵敏度：95%vs.79%）。
结论：AI辅助内镜诊断胃肠上皮化生具有较高的表现和临床诊断价值。然而,需要进一步的前瞻性研究来验证这些发现.
