Background: Colonoscopy reduces the incidence of colorectal cancer (CRC) by allowing detection and resection of neoplastic polyps. Evidence shows that many small polyps are missed on a single colonoscopy. There has been a successful adoption of artificial intelligence (AI) technologies to tackle the issues around missed polyps and as tools to increase the adenoma detection rate (ADR). Objective: The aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps. Methods: A comprehensive literature search was undertaken using the databases of Embase, MEDLINE, and the Cochrane Library. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Studies reporting the use of computer-aided diagnosis for polyp detection or characterization during colonoscopy were included. Independent proportions and their differences were calculated and pooled through DerSimonian and Laird random-effects modeling. Results: A total of 48 studies were included. The meta-analysis showed a significant increase in pooled polyp detection rate in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (odds ratio [OR] 1.75, 95% CI 1.56-1.96; P<.001). When comparing patients undergoing colonoscopy with the use of AI to those without, there was also a significant increase in ADR (OR 1.53, 95% CI 1.32-1.77; P<.001). Conclusions: With the aid of machine learning, there is potential to improve ADR and, consequently, reduce the incidence of CRC. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterization of colorectal polyps. However, this is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786

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中文翻译：

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人工智能和计算机辅助诊断对结直肠息肉检测和表征的诊断准确性：系统评价和荟萃分析


背景：结肠镜检查可以检测和切除肿瘤性息肉，从而降低结直肠癌 (CRC) 的发病率。有证据表明，单次结肠镜检查会漏掉许多小息肉。人工智能（AI）技术已被成功采用来解决遗漏息肉的问题，并作为提高腺瘤检出率（ADR）的工具。目的：本次综述的目的是检验基于人工智能的技术在评估结直肠息肉方面的诊断准确性。方法：使用 Embase、MEDLINE 和 Cochrane 图书馆的数据库进行全面的文献检索。遵循 PRISMA（系统评价和荟萃分析的首选报告项目）指南。报告了在结肠镜检查期间使用计算机辅助诊断进行息肉检测或表征的研究也被纳入其中。通过 DerSimonian 和 Laird 随机效应模型计算并汇总独立比例及其差异。结果：共纳入 48 项研究。荟萃分析显示，与接受标准结肠镜检查的患者相比，在结肠镜检查期间使用 AI 进行息肉检测的患者的汇总息肉检出率显着增加（比值比 [OR] 1.75，95% CI 1.56-1.96；P<0.01）。 001）。将接受结肠镜检查并使用 AI 的患者与未使用 AI 的患者进行比较时，ADR 也显着增加（OR 1.53，95% CI 1.32-1.77；P<.001）。结论：借助机器学习，有可能改善 ADR，从而降低 CRC 的发病率。当前一代基于人工智能的系统在结直肠息肉的检测和表征方面表现出令人印象深刻的准确性。 然而，这是一个不断发展的领域，在应用于临床环境之前，人工智能系统必须证明对患者和临床医生有价值。试用注册：PROSPERO 国际前瞻性系统评价注册库 CRD42020169786； https://www.crd.york.ac.uk/prospero/display_record.php？ID=CRD42020169786


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