Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence
Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence
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DOI：
10.1148/radiol.2020202439
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发表时间：
2021-01-01
期刊：
影响因子：
19.7
通讯作者：
van Ginneken, Bram
中科院分区：
文献类型：
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作者：
Lessmann, Nikolas;Sanchez, Clara, I;van Ginneken, Bram
背景：2019冠状病毒病（COVID-19）大流行以惊人的速度、发病率和死亡率在全球蔓延。当最终病毒检测结果延迟时，使用胸部CT对疑似COVID-19引起的胸部感染患者进行立即分诊可能会有所帮助。目的：开发和验证一种人工智能（AI）系统，使用COVID-19报告和数据系统（CO-RADS）和CT严重程度评分系统对胸部CT扫描中肺部COVID-19的可能性和程度进行评分。材料与方法：CO-RADS人工智能系统由三种深度学习算法组成，可自动分割5个肺叶，对疑似COVID-19进行CO-RADS评分，并对每个肺叶的实质受累程度进行CT严重程度评分。本研究回顾性纳入了在两个医疗中心因临床怀疑COVID-19而接受非增强胸部CT检查的患者。使用其中一个中心的数据对该系统进行了培训、验证和测试。来自第二个中心的数据作为外部测试集。诊断表现和与8名独立观察员分配的分数的一致性使用受试者工作特征分析，线性加权kappa值和分类准确性进行测量。结果：共有105例患者（平均年龄62岁+/- 16岁，男性61例）和262例患者（平均年龄64岁+/- 16岁，男性154例）分别接受了内部和外部测试集的评估。该系统区分了COVID-19患者和非COVID-19患者，内部和外部测试集的受试者工作特征曲线下面积分别为0.95 （95% CI: 0.91, 0.98）和0.88 （95% CI: 0.84, 0.93）。与8名人类观察者的一致程度中等至相当，CO-RADS评分的平均线性加权k值为0.60 +/- 0.01，CT严重程度评分为0.54 +/- 0.01。结论：CO-RADS AI系统具有较高的诊断性能，可通过胸部CT扫描正确识别COVID-19患者，并分配标准化的CO-RADS和CT严重程度评分，该评分与8名独立观察者的结果一致，并可很好地推广到外部数据。(c) rsna, 2020
Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy.Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores.Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 2020