Stanford University, Stanford, CA, USA.
Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Sci Rep. 2022 Apr 21;12(1):6598. doi: 10.1038/s41598-022-10597-y.
Artificial intelligence (AI) has increasingly been employed in multiple fields, and there has been significant interest in its use within gastrointestinal endoscopy. Computer-aided detection (CAD) can potentially improve polyp detection rates and decrease miss rates in colonoscopy. However, few clinical studies have evaluated real-time CAD during colonoscopy. In this study, we analyze the efficacy of a novel real-time CAD system during colonoscopy. This was a single-arm prospective study of patients undergoing colonoscopy with a real-time CAD system. This AI-based system had previously been trained using manually labeled colonoscopy videos to help detect neoplastic polyps (adenomas and serrated polyps). In this pilot study, 300 patients at two centers underwent elective colonoscopy with the CAD system. These results were compared to 300 historical controls consisting of consecutive colonoscopies performed by the participating endoscopists within 12 months prior to onset of the study without the aid of CAD. The primary outcome was the mean number of adenomas per colonoscopy. Use of real-time CAD trended towards increased adenoma detection (1.35 vs 1.07, p = 0.099) per colonoscopy though this did not achieve statistical significance. Compared to historical controls, use of CAD demonstrated a trend towards increased identification of serrated polyps (0.15 vs 0.07) and all neoplastic (adenomatous and serrated) polyps (1.50 vs 1.14) per procedure. There were significantly more non-neoplastic polyps detected with CAD (1.08 vs 0.57, p < 0.0001). There was no difference in ≥ 10 mm polyps identified between the two groups. A real-time CAD system can increase detection of adenomas and serrated polyps during colonoscopy in comparison to historical controls without CAD, though this was not statistically significant. As this pilot study is underpowered, given the findings we recommend pursuing a larger randomized controlled trial to further evaluate the benefits of CAD.
人工智能（AI）已越来越多地应用于多个领域，人们对其在胃肠内镜中的应用产生了浓厚的兴趣。计算机辅助检测（CAD）有可能提高结肠镜检查中息肉的检出率并降低漏诊率。然而，很少有临床研究评估过结肠镜检查中的实时 CAD。在这项研究中，我们分析了一种新型实时 CAD 系统在结肠镜检查中的效果。这是一项对接受实时 CAD 系统结肠镜检查的患者进行的单臂前瞻性研究。这个基于人工智能的系统之前已经使用手动标记的结肠镜视频进行了训练，以帮助检测肿瘤性息肉（腺瘤和锯齿状息肉）。在这项初步研究中，两个中心的 300 名患者接受了 CAD 系统的选择性结肠镜检查。这些结果与在研究开始前 12 个月内由参与的内镜医生进行的 300 例连续结肠镜检查的历史对照进行了比较，这些结肠镜检查没有 CAD 的帮助。主要结局是每例结肠镜检查的平均腺瘤数。尽管使用实时 CAD 检测的腺瘤数量呈增加趋势（1.35 与 1.07，p=0.099），但这并未达到统计学意义。与历史对照相比，CAD 的使用显示出增加锯齿状息肉（0.15 与 0.07）和所有肿瘤性（腺瘤性和锯齿状）息肉（1.50 与 1.14）检出的趋势。使用 CAD 检测到的非肿瘤性息肉明显更多（1.08 与 0.57，p<0.0001）。两组之间在≥10mm 息肉的检出率方面没有差异。与没有 CAD 的历史对照相比，实时 CAD 系统可以增加结肠镜检查中腺瘤和锯齿状息肉的检出率，尽管这没有统计学意义。由于这项初步研究的效力不足，根据研究结果，我们建议进行更大规模的随机对照试验，以进一步评估 CAD 的益处。
