Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma
Artificial intelligence diagnostic system predicts multiple Lugol-voiding lesions in the esophagus and patients at high risk for esophageal squamous cell carcinoma
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DOI：
10.1055/a-1334-4053
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发表时间：
2021-02-04
期刊：
影响因子：
9.3
通讯作者：
Fujisaki, Junko
中科院分区：
文献类型：
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作者：
Ikenoyama, Yohei;Yoshio, Toshiyuki;Fujisaki, Junko
背景：众所周知，碘染色后食管有多个卢戈排泄性病变（LVL）是食管癌的高风险;然而，最好在没有染色的情况下识别高风险病例，因为碘会导致不适和缩短检查时间。本研究评估了人工智能（AI）系统的能力来预测多个LVL的图像，没有被染色碘以及患者在高风险的食管cancer.Methods我们构建了AI系统，准备一个训练集的6634图像从白光和窄带成像在595例患者之前，他们接受了内镜检查碘染色。在独立的验证数据集上评价诊断性能结果AI系统预测多个LVL的敏感性、特异性和准确性分别为84.4%、70.0%和76.4%，与之相比，分别为46.9%、77.5%和63.9%，给内窥镜医生AI系统的灵敏度显著高于9/10的经验丰富的内窥镜医生。我们还确定了6种内镜检查结果，这些结果在多发性LVL患者中明显更常见;然而，AI系统在预测多发性LVL方面的灵敏度高于这些结果。结论人工智能系统可以在不进行碘染色的情况下，以高灵敏度从图像中预测多个LVL。该系统可以使内窥镜医生更明智地应用碘染色。
Background It is known that an esophagus with multiple Lugol-voiding lesions (LVLs) after iodine staining is high risk for esophageal cancer; however, it is preferable to identify high-risk cases without staining because iodine causes discomfort and prolongs examination times. This study assessed the capability of an artificial intelligence (AI) system to predict multiple LVLs from images that had not been stained with iodine as well as patients at high risk for esophageal cancer.Methods We constructed the AI system by preparing a training set of 6634 images from white-light and narrow-band imaging in 595 patients before they underwent endoscopic examination with iodine staining. Diagnostic performance was evaluated on an independent validation dataset (667 images from 72 patients) and compared with that of 10 experienced endoscopists.Results The sensitivity, specificity, and accuracy of the AI system to predict multiple LVLs were 84.4%, 70.0%, and 76.4%, respectively, compared with 46.9%, 77.5%, and 63.9%, respectively, for the endoscopists. The AI system had significantly higher sensitivity than 9/10 experienced endoscopists. We also identified six endoscopic findings that were significantly more frequent in patients with multiple LVLs; however, the AI system had greater sensitivity than these findings for the prediction of multiple LVLs. Moreover, patients with AI-predicted multiple LVLs had significantly more cancers in the esophagus and head and neck than patients without predicted multiple LVLs.Conclusion The AI system could predict multiple LVLs with high sensitivity from images without iodine staining. The system could enable endoscopists to apply iodine staining more judiciously.