Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality
Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality
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DOI：
10.1097/mcg.0000000000001272
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发表时间：
2020-07-01
影响因子：
2.9
通讯作者：
Berzin, Tyler M.
中科院分区：
文献类型：
--
作者：
Becq, Aymeric;Chandnani, Madhuri;Berzin, Tyler M.
背景：结肠镜检查是发现息肉的金标准，但息肉可能会被漏诊。人工智能(AI)技术可能有助于息肉检测。到目前为止，大多数息肉检测研究都在理想的内窥镜条件下验证了算法。目的：评估深度学习算法在具有可变肠道准备质量的常规结肠镜检查的真实环境中的性能。方法：我们对50名连续接受结肠镜检查的患者进行了前瞻性、单中心研究。程序视频由一个经过验证的深度学习AI息肉检测软件进行分析，该软件标记了可疑的息肉。然后，5名经验丰富的内窥镜医生重新阅读视频，对内窥镜医生和/或人工智能识别的所有可能的息肉进行分类，并测量波士顿肠道准备评分。结果：经鼻内窥镜检查发现并摘除息肉55例。人工智能系统识别出401个可能的息肉。共有100例(24.9%)被归类为“明确的息肉”，其中53/100被内窥镜医生识别并切除。共有63例(15.6%)被归类为“可能的息肉”，未经内窥镜医生切除。总共有238/401被归类为假阳性。内窥镜检查发现的两个息肉被人工智能漏掉(假阴性)。AI检测息肉的敏感性为98.8%，阳性预测值为40.6%。内窥镜的息肉检测率为62%，而人工智能系统的息肉检测率为82%。真阳性和假阳性的平均波士顿肠段准备评分相似(2.64，2.59，P=0.47)。结论：深度学习算法可以有效地在前瞻性收集的一系列结肠镜检查中检测息肉，即使在可变准备质量的情况下也是如此。
Background: Colonoscopy is the gold standard for polyp detection, but polyps may be missed. Artificial intelligence (AI) technologies may assist in polyp detection. To date, most studies for polyp detection have validated algorithms in ideal endoscopic conditions. Aim: To evaluate the performance of a deep-learning algorithm for polyp detection in a real-world setting of routine colonoscopy with variable bowel preparation quality. Methods: We performed a prospective, single-center study of 50 consecutive patients referred for colonoscopy. Procedural videos were analyzed by a validated deep-learning AI polyp detection software that labeled suspected polyps. Videos were then re-read by 5 experienced endoscopists to categorize all possible polyps identified by the endoscopist and/or AI, and to measure Boston Bowel Preparation Scale. Results: In total, 55 polyps were detected and removed by the endoscopist. The AI system identified 401 possible polyps. A total of 100 (24.9%) were categorized as "definite polyps;" 53/100 were identified and removed by the endoscopist. A total of 63 (15.6%) were categorized as "possible polyps" and were not removed by the endoscopist. In total, 238/401 were categorized as false positives. Two polyps identified by the endoscopist were missed by AI (false negatives). The sensitivity of AI for polyp detection was 98.8%, the positive predictive value was 40.6%. The polyp detection rate for the endoscopist was 62% versus 82% for the AI system. Mean segmental Boston Bowel Preparation Scale were similar (2.64, 2.59,P=0.47) for true and false positives, respectively. Conclusions: A deep-learning algorithm can function effectively to detect polyps in a prospectively collected series of colonoscopies, even in the setting of variable preparation quality.