Objectives To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT. Methods A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS-2 and QUADAS-C were used to assess risk of bias. We undertook narrative synthesis. Results Eleven studies evaluating six different AI-based software and reporting on 19 770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared with unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (−7% to −3% for correctly detecting/categorising people without actionable nodules; −8% to −6% for correctly detecting/categorising people without malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150–750 cancers detected per million people attending screening but lead to an additional 59 700 to 79 600 people attending screening without cancer receiving unnecessary CT surveillance. Conclusions AI assistance in lung cancer screening may improve sensitivity but increases the number of false-positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design. PROSPERO registration number CRD42021298449. All data relevant to the study are included in the article or uploaded as supplementary information.

中文翻译：

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在CT肺癌筛查中使用人工智能检测结节和癌症的软件：测试准确性研究的系统评价


目的 检查人工智能 （AI） 软件辅助使用 CT 进行肺癌筛查的准确性和影响。方法 对带有 CE 标志的、基于 AI 的软件进行系统评价，该软件用于 CT 肺癌筛查中结节的自动检测和分析。检索了 2012 年至 2023 年 3 月的多个数据库，包括 Medline、Embase 和 Cochrane CENTRAL。包括报告测试准确性或对阅读时间或临床管理影响的主要研究。使用 QUADAS-2 和 QUADAS-C 评估偏倚风险。我们进行了叙述综合。结果 11 项研究评估了 6 种不同的基于 AI 的软件并报告了 19 770 名患者，符合条件。所有研究均存在高偏倚风险，存在多种适用性问题。与无辅助阅读相比，AI 辅助阅读更快，灵敏度普遍提高（+5% 至 +20% 用于检测/分类可操作结节;+3% 至 +15% 用于检测/分类恶性结节），特异性较低（正确检测/分类无可操作结节的人为 -7% 至 -3%;正确检测/分类无恶性结节的人为 -8% 至 -6%）。AI 辅助往往会增加分配给高风险类别的结节比例。假设癌症患病率为 0.5%，这些结果将转化为每百万参加筛查的人额外检测到 150-750 例癌症，但会导致额外的 59 700 至 79 600 人参加筛查，而癌症没有接受不必要的 CT 监测。结论 AI 辅助肺癌筛查可能会提高敏感性，但会增加假阳性结果和不必要的监测数量。 未来的研究需要提高 AI 辅助阅读的特异性，并通过改进研究设计来最大限度地降低偏倚风险和适用性问题。PROSPERO 注册号 CRD42021298449。与研究相关的所有数据都包含在文章中或作为补充信息上传。