Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.
Graduate School of Informatics, Nagoya University, Aichi, Japan.
Dig Endosc. 2019 Jul;31(4):363-371. doi: 10.1111/den.13340. Epub 2019 Feb 27.
Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps.
Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy.
We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence-assisted medical devices.
人工智能在医学中的应用正引起广泛关注。在胃肠内镜领域，结肠镜计算机辅助诊断（CAD）是研究最多的领域，尽管它仍处于临床前阶段。由于结肠镜检查由人执行，因此其本质上是一个不完善的过程。CAD 辅助有望提高其在自动化息肉检测和特征描述（即预测息肉的病理学）方面的质量。它可以帮助防止内窥镜医师遗漏息肉，并为检测到的息肉提供准确的光学诊断。最终，CAD 提供的这些功能可以提高腺瘤检出率，降低用于治疗增生性息肉的息肉切除术的成本。
目前，自动化息肉检测的研究仅限于使用基于离体视频或静态图像的算法进行实验评估。据报道，其在临床应用中的性能具有 >90%的灵敏度和可接受的特异性。相比之下，自动化息肉特征描述的研究似乎超过了息肉检测。几个研究小组已经报告了人工智能技术在体内使用的前瞻性研究，其中一些研究显示在区分直肠乙状结肠小（≤5mm）腺瘤时，阴性预测值 >90%，超过了光学活检的阈值。
我们介绍了 CAD 在结肠镜检查中的应用潜力，并描述了人工智能辅助医疗器械最新的监管审批情况。
