{"title":"【新冠肺炎影像诊断AI应用前沿】。","authors":"Hidetaka Arimura, Takahiro Iwasaki","doi":"10.11323/jjmp.41.3_82","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13394,"journal":{"name":"Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics","volume":"41 3","pages":"82-86"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Forefront of AI Applications for COVID-19 Imaging Diagnosis].\",\"authors\":\"Hidetaka Arimura, Takahiro Iwasaki\",\"doi\":\"10.11323/jjmp.41.3_82\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":13394,\"journal\":{\"name\":\"Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics\",\"volume\":\"41 3\",\"pages\":\"82-86\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11323/jjmp.41.3_82\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11323/jjmp.41.3_82","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
[Forefront of AI Applications for COVID-19 Imaging Diagnosis].
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.