Arimura Hidetaka, Iwasaki Takahiro
Kyushu University.
The University of Tokyo Hospital.
Igaku Butsuri. 2021;41(3):82-86. doi: 10.11323/jjmp.41.3_82.
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.
胸部CT图像诊断中观察者内和观察者间的变异性可能会影响新型冠状病毒肺炎（COVID-19）的诊断。因此，已有多项研究报道利用深度学习（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）和手工提取（放射组学）。基于AI的成像方法对COVID-19的优势在于通量高、非侵入性、可量化以及能整合PCR结果、CT表现和临床信息。据我所知，已研究了三种类型的AI方法：COVID-19的检测、严重程度区分和预后预测。在COVID-19成为常见疾病之一后，AI技术对COVID-19严重程度评估/预后预测可能比检测COVID-19肺炎更为关键。
