Ishiwata Yoshinobu, Miura Kentaro, Kishimoto Mayuko, Nomura Koichiro, Sawamura Shungo, Magami Shigeru, Ikawa Mizuki, Yamashiro Tsuneo, Utsunomiya Daisuke
Department of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama 236-0004, Japan.
Department of Radiology, Yokohama Municipal Citizen's Hospital, 1-1 Mitsuzawa Nishimachi, Kanagawa-ku, Yokohama 221-0855, Japan.
Diagnostics (Basel). 2022 Mar 18;12(3):738. doi: 10.3390/diagnostics12030738.
In this study, we first developed an artificial intelligence (AI)-based algorithm for classifying chest computed tomography (CT) images using the coronavirus disease 2019 Reporting and Data System (CO-RADS). Subsequently, we evaluated its accuracy by comparing the calculated scores with those assigned by radiologists with varying levels of experience. This study included patients with suspected SARS-CoV-2 infection who underwent chest CT imaging between February and October 2020 in Japan, a non-endemic area. For each chest CT, the CO-RADS scores, determined by consensus among three experienced chest radiologists, were used as the gold standard. Images from 412 patients were used to train the model, whereas images from 83 patients were tested to obtain AI-based CO-RADS scores for each image. Six independent raters (one medical student, two residents, and three board-certified radiologists) evaluated the test images. Intraclass correlation coefficients (ICC) and weighted kappa values were calculated to determine the inter-rater agreement with the gold standard. The mean ICC and weighted kappa were 0.754 and 0.752 for the medical student and residents (taken together), 0.851 and 0.850 for the diagnostic radiologists, and 0.913 and 0.912 for AI, respectively. The CO-RADS scores calculated using our AI-based algorithm were comparable to those assigned by radiologists, indicating the accuracy and high reproducibility of our model. Our study findings would enable accurate reading, particularly in areas where radiologists are unavailable, and contribute to improvements in patient management and workflow.
在本研究中，我们首先开发了一种基于人工智能（AI）的算法，用于使用2019冠状病毒病报告和数据系统（CO-RADS）对胸部计算机断层扫描（CT）图像进行分类。随后，我们通过将计算出的分数与不同经验水平的放射科医生给出的分数进行比较，评估了其准确性。本研究纳入了2020年2月至10月在日本（一个非流行地区）接受胸部CT成像的疑似SARS-CoV-2感染患者。对于每例胸部CT，由三位经验丰富的胸部放射科医生通过共识确定的CO-RADS分数用作金标准。来自412例患者的图像用于训练模型，而来自83例患者的图像用于测试，以获得每张图像基于AI的CO-RADS分数。六位独立评估者（一名医学生、两名住院医师和三名获得委员会认证的放射科医生）对测试图像进行了评估。计算组内相关系数（ICC）和加权kappa值，以确定评估者与金标准之间的一致性。医学生和住院医师（合在一起）的平均ICC和加权kappa分别为0.754和0.752，诊断放射科医生为0.851和0.850，AI为0.913和0.912。使用我们基于AI的算法计算出的CO-RADS分数与放射科医生给出的分数相当，表明我们模型的准确性和高重复性。我们的研究结果将有助于进行准确的解读，特别是在没有放射科医生的地区，并有助于改善患者管理和工作流程。
