Background and Aims

We performed a meta-analysis of all published studies to determine the diagnostic accuracy of artificial intelligence (AI) on histology prediction and detection of colorectal polyps.

Method

We searched Embase, PubMed, Medline, Web of Science, and Cochrane library databases to identify studies using AI for colorectal polyp histology prediction and detection. The quality of included studies was measured by the Quality Assessment of Diagnostic Accuracy Studies tool. We used a bivariate meta-analysis following a random-effects model to summarize the data and plotted hierarchical summary receiver operating characteristic curves. The area under the hierarchical summary receiver operating characteristic curve (AUC) served as an indicator of the diagnostic accuracy and during head-to-head comparisons.

Results

A total of 7680 images of colorectal polyps from 18 studies were included in the analysis of histology prediction. The accuracy of the AI (AUC) was .96 (95% confidence interval [CI], .95-.98), with a corresponding pooled sensitivity of 92.3% (95% CI, 88.8%-94.9%) and specificity of 89.8% (95% CI, 85.3%-93.0%). The AUC of AI using narrow-band imaging (NBI) was significantly higher than the AUC using non-NBI (.98 vs .84, P < .01). The performance of AI was superior to nonexpert endoscopists (.97 vs .90, P < .01). For characterization of diminutive polyps using a deep learning model with nonmagnifying NBI, the pooled negative predictive value was 95.1% (95% CI, 87.7%-98.1%). For polyp detection, the pooled AUC was .90 (95% CI, .67-1.00) with a sensitivity of 95.0% (95% CI, 91.0%-97.0%) and a specificity of 88.0% (95% CI, 58.0%-99.0%).

Conclusions

AI was accurate in histology prediction and detection of colorectal polyps, including diminutive polyps. The performance of AI was better under NBI and was superior to nonexpert endoscopists. Despite the difference in AI models and study designs, AI performances are rather consistent, which could serve as a reference for future AI studies.

中文翻译：

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人工智能对大肠息肉的组织学预测和检测的准确性：系统评价和荟萃分析。

背景和目标

我们对所有已发表研究进行了荟萃分析，以确定人工智能（AI）在组织学预测和结直肠息肉检测中的诊断准确性。

方法

我们搜索了Embase，PubMed，Medline，Web of Science和Cochrane库数据库，以确定使用AI进行结直肠息肉组织学预测和检测的研究。纳入研究的质量通过诊断准确性研究的质量评估工具进行衡量。我们使用遵循随机效应模型的二元荟萃分析对数据进行汇总，并绘制分层汇总接收器工作特性曲线。分层汇总接收器工作特性曲线（AUC）下的区域可作为诊断准确性以及进行头对头比较的指标。

结果

组织学预测分析共包括来自18个研究的7680份结肠直肠息肉图像。AI（AUC）的准确性为0.96（95％置信区间[CI] ，. 95-.98），相应的合并敏感性为92.3％（95％CI，88.8％-94.9％），特异性为89.8 ％（95％CI，85.3％-93.0％）。使用窄带成像（NBI）的AI的AUC显着高于使用非NBI的AI的AUC（.98 vs.84，P  <.01）。AI的性能优于非内镜专家（.97 vs .90，P <.01）。对于使用非放大NBI的深度学习模型表征微小息肉，合并的阴性预测值为95.1％（95％CI，87.7％-98.1％）。对于息肉检测，合并的AUC为.90（95％CI，.67-1.00），灵敏度为95.0％（95％CI，91.0％-97.0％），特异性为88.0％（95％CI，58.0％） -99.0％）。

结论

AI在包括小肠息肉在内的大肠息肉的组织学预测和检测中是准确的。在NBI下AI的性能更好，并且优于非专业内镜医师。尽管AI模型和研究设计存在差异，但AI的性能还是相当一致的，可以为以后的AI研究提供参考。