Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation
Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation
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DOI：
10.1111/den.13934
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发表时间：
2021-02-27
影响因子：
5.3
通讯作者：
Tada, Tomohiro
中科院分区：
文献类型：
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作者：
Waki, Kotaro;Ishihara, Ryu;Tada, Tomohiro
目的人工智能（AI）系统在食管鳞状细胞癌（ESCC）的检测中表现出良好的性能。然而，以前的研究受到其验证方法质量的限制。在这项研究中，我们评估了人工智能系统的性能与视频模拟的情况下，ESCC一直被忽视。方法我们使用了17,336图像从1376浅表ESCC和1461图像从196非癌和正常食管构建人工智能系统。为了记录验证视频，内窥镜以恒定速度通过食管，而不聚焦于病变，以模拟ESCC被遗漏的情况。结果我们准备了100个视频数据集，包括50个浅表ESCC，22个非癌性病变和28个正常食管。AI系统的敏感性为85.7%（63例ESCC中的54例），特异性为40%。内窥镜医师使用普通视频（无AI支持）进行的初始评估的平均灵敏度为75.0%（63例ESCC中的47.3例），特异性为91.4%。内镜医师在AI辅助下进行后续评估，其敏感性提高到77.7%（P = 0.00696），而特异性（91.6%，P = 0.756）没有改变。作为一种支持工具，该系统有可能提高ESCC的检测而不降低特异性。（UMIN000039645）
Objectives Artificial intelligence (AI) systems have shown favorable performance in the detection of esophageal squamous cell carcinoma (ESCC). However, previous studies were limited by the quality of their validation methods. In this study, we evaluated the performance of an AI system with videos simulating situations in which ESCC has been overlooked.Methods We used 17,336 images from 1376 superficial ESCCs and 1461 images from 196 noncancerous and normal esophagi to construct the AI system. To record validation videos, the endoscope was passed through the esophagus at a constant speed without focusing on the lesion to simulate situations in which ESCC has been missed. Validation videos were evaluated by the AI system and 21 endoscopists.Results We prepared 100 video datasets, including 50 superficial ESCCs, 22 noncancerous lesions, and 28 normal esophagi. The AI system had sensitivity of 85.7% (54 of 63 ESCCs) and specificity of 40%. Initial evaluation by endoscopists conducted with plain video (without AI support) had average sensitivity of 75.0% (47.3 of 63 ESCC) and specificity of 91.4%. Subsequent evaluation by endoscopists was conducted with AI assistance, which improved their sensitivity to 77.7% (P = 0.00696) without changing their specificity (91.6%, P = 0.756).Conclusions Our AI system had high sensitivity for the detection of ESCC. As a support tool, the system has the potential to enhance detection of ESCC without reducing specificity. (UMIN000039645)