Background and Aims

Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR.

Methods

We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant.

Results

We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595–0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688–0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9–1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer.

Conclusion

Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.

中文翻译：

---

单一观察者、第二观察者与人工智能提高结肠镜检查腺瘤检出率的网络分析

背景和目标

以腺瘤检出率（ADR）作为质量标志，筛查结肠镜检查对降低结直肠癌（CRC）的发病率及其相关死亡率做出了显着贡献。为了提高 ADR，人们提出了各种解决方案，包括利用人工智能 (AI) 和在结肠镜检查期间雇用第二观察员。为了让人工智能在没有第二个观察者的情况下独立改善 ADR，以及人工智能和第二个观察者之间的操作相似性，该网络元分析旨在评估人工智能、第二个观察者和单个观察者在改善 ADR 方面的有效性。

方法

我们检索了 Medline、Embase、Cochrane、Web of Science 核心合集、韩国引文索引、SciELO、Global Index Medicus 和 Cochrane。使用随机效应模型进行直接头对头比较分析和网络荟萃分析。使用 95% 置信区间 (CI) 计算比值比 (OR)，p值 < 0.05 被认为具有统计显着性。

结果

我们分析了 26 项研究，涉及 22,560 名受试者。 在直接比较分析中，AI 表现出比单一观察者更高的 ADR（OR：0.668，95% CI 0.595-0.749，p < 0.001）。双重观察者表现出比单一操作者更高的 ADR（OR：0.771，95% CI 0.688–0.865，p  < 0.001）。在网络荟萃分析中，结果与网络荟萃分析一致，保持了一致性。将人工智能与第二个观察者进行比较时，没有发现统计差异。（RR 1.1（0.9-1.2，p  = 0.3）。仅评估随机对照试验时，结果是一致的。净排名为 AI 提供了更高的分数，其次是第二个观察者，然后是单个观察者。

结论

与单观察者结肠镜检查相比，人工智能和第二观察者结肠镜检查在腺瘤检出率方面显示出更高的成功率。尽管在统计上不显着，但净排名模型有利于人工智能相对于第二个观察者的优越性。