Department of Biomedical Sciences, Humanitas University, Milan, Italy
Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy.
Gut. 2022 Apr;71(4):757-765. doi: 10.1136/gutjnl-2021-324471. Epub 2021 Jun 29.
Artificial intelligence has been shown to increase adenoma detection rate (ADR) as the main surrogate outcome parameter of colonoscopy quality. To which extent this effect may be related to physician experience is not known. We performed a randomised trial with colonoscopists in their qualification period (AID-2) and compared these data with a previously published randomised trial in expert endoscopists (AID-1).
In this prospective, randomised controlled non-inferiority trial (AID-2), 10 non-expert endoscopists (<2000 colonoscopies) performed screening/surveillance/diagnostic colonoscopies in consecutive 40-80 year-old subjects using high-definition colonoscopy with or without a real-time deep-learning computer-aided detection (CADe) (GI Genius, Medtronic). The primary outcome was ADR in both groups with histology of resected lesions as reference. In a post-hoc analysis, data from this randomised controlled trial (RCT) were compared with data from the previous AID-1 RCT involving six experienced endoscopists in an otherwise similar setting.
In 660 patients (62.3±10 years; men/women: 330/330) with equal distribution of study parameters, overall ADR was higher in the CADe than in the control group (53.3% vs 44.5%; relative risk (RR): 1.22; 95% CI: 1.04 to 1.40; p<0.01 for non-inferiority and p=0.02 for superiority). Similar increases were seen in adenoma numbers per colonoscopy and in small and distal lesions. No differences were observed with regards to detection of non-neoplastic lesions. When pooling these data with those from the AID-1 study, use of CADe (RR 1.29; 95% CI: 1.16 to 1.42) and colonoscopy indication, but not the level of examiner experience (RR 1.02; 95% CI: 0.89 to 1.16) were associated with ADR differences in a multivariate analysis.
In less experienced examiners, CADe assistance during colonoscopy increased ADR and a number of related polyp parameters as compared with the control group. Experience appears to play a minor role as determining factor for ADR.
NCT:04260321.
人工智能已被证明可以提高腺瘤检出率（ADR），作为结肠镜质量的主要替代结局参数。但尚不清楚这种效果在多大程度上与医生的经验有关。我们进行了一项纳入处于资格期的结肠镜医师的随机试验（AID-2），并将这些数据与之前发表的一项纳入专家内镜医师的随机试验（AID-1）进行比较。
在这项前瞻性、随机对照非劣效性试验（AID-2）中，10 名非专家内镜医师（<2000 例结肠镜检查）在 40-80 岁的连续受试者中使用高清结肠镜检查，同时使用或不使用实时深度学习计算机辅助检测（CADe）（GI Genius，美敦力）。主要结局是两组的 ADR，以切除病变的组织学为参考。在事后分析中，将这项随机对照试验（RCT）的数据与之前的 AID-1 RCT 数据进行了比较，该 RCT 纳入了 6 名在类似环境下具有丰富经验的内镜医师。
在 660 例患者（62.3±10 岁；男/女：330/330）中，研究参数均衡分布，CADe 组的总体 ADR 高于对照组（53.3% vs 44.5%；相对风险（RR）：1.22；95%CI：1.04 至 1.40；p<0.01 表示非劣效性，p=0.02 表示优效性）。结肠镜检查中腺瘤数量、小腺瘤和远端腺瘤的增加情况相似。非肿瘤性病变的检出率无差异。当将这些数据与 AID-1 研究的数据进行汇总时，CADe 的使用（RR 1.29；95%CI：1.16 至 1.42）和结肠镜检查适应证，但不是检查者经验水平（RR 1.02；95%CI：0.89 至 1.16）与多变量分析中的 ADR 差异相关。
在经验较少的检查者中，与对照组相比，CADe 辅助结肠镜检查可提高 ADR 和相关息肉参数的数量。经验似乎是决定 ADR 的一个次要因素。
NCT:04260321。
