[Forefront of AI Applications for COVID-19 Imaging Diagnosis].

Hidetaka Arimura, Takahiro Iwasaki
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Abstract

The intra- and inter-observer variability in diagnosis of thoracic CT images may affect the diagnosis of COVID-19. Therefore, several studies have been reported to develop artificial intelligence (AI) approaches using deep learning (DL) and radiomics technologies. The difference between them is automatic feature extraction (DL) and hand-crafted one (radiomics). The advantages of the AI-based imaging approaches for the COVID-19 are fast throughput, non-invasion, quantification, and integration of PCR results, CT findings, and clinical information. To the best of my knowledge, three types of the AI approaches have been studied: detection, severity differentiation, and prognosis prediction of COVID-19. AI technologies on assessment of severity/prediction of prognosis for COVID-19 may be more crucial than detection of COVID-19 pneumonia after COVID-19 becomes one of common diseases.

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【新冠肺炎影像诊断AI应用前沿】。
胸部CT图像诊断的观察者内部和观察者之间的差异可能会影响COVID-19的诊断。因此,有几项研究报道使用深度学习(DL)和放射组学技术开发人工智能(AI)方法。它们之间的区别在于自动特征提取(DL)和手工特征提取(radiomics)。基于人工智能的新冠肺炎成像方法具有通量快、无侵入性、可量化、可整合PCR结果、CT表现和临床信息等优点。据我所知,目前已经研究了三种类型的人工智能方法:COVID-19的检测、严重程度区分和预后预测。在COVID-19成为常见病后,AI技术对COVID-19严重程度评估/预后预测可能比COVID-19肺炎的检测更为重要。
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