The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

中文翻译：

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利用人工智能从胸部 CT 图像诊断 COVID-19 的综述


COVID-19 诊断方法主要分为两大类：基于实验室的诊断方法和胸部 X 光检查方法。过去几个月，使用人工智能 (AI) 技术通过胸部计算机断层扫描 (CT) 诊断 COVID-19 的研究数量迅速增加。在这项研究中，我们通过胸部 CT 和 AI 来回顾对 COVID-19 的诊断。我们使用“深度学习”、“神经网络”、“COVID-19”和“胸部 CT”等术语搜索了 ArXiv、MedRxiv 和 Google Scholar。截至撰写本文时（2020年8月24日），已有近100项研究，其中30项研究入选本次综述。我们根据分类任务对研究进行了分类：Covid-19/正常、Covid-19/非Covid-19、Covid-19/非Covid-19肺炎和严重程度。敏感性、特异性、精密度、准确度、曲线下面积和 F1 评分结果分别高达 100%、100%、99.62、99.87%、100% 和 99.5%。然而，由于不同分类任务的难度不同，应仔细比较所呈现的结果。