{"title":"为 COVID-19 开发基于深度学习的计算机断层扫描分类系统并进行外部验证。","authors":"Yuki Kataoka, Tomohisa Baba, Tatsuyoshi Ikenoue, Yoshinori Matsuoka, Junichi Matsumoto, Junji Kumasawa, Kentaro Tochitani, Hiraku Funakoshi, Tomohiro Hosoda, Aiko Kugimiya, Michinori Shirano, Fumiko Hamabe, Sachiyo Iwata, Yoshiro Kitamura, Tsubasa Goto, Shingo Hamaguchi, Takafumi Haraguchi, Shungo Yamamoto, Hiromitsu Sumikawa, Koji Nishida, Haruka Nishida, Koichi Ariyoshi, Hiroaki Sugiura, Hidenori Nakagawa, Tomohiro Asaoka, Naofumi Yoshida, Rentaro Oda, Takashi Koyama, Yui Iwai, Yoshihiro Miyashita, Koya Okazaki, Kiminobu Tanizawa, Tomohiro Handa, Shoji Kido, Shingo Fukuma, Noriyuki Tomiyama, Toyohiro Hirai, Takashi Ogura","doi":"10.37737/ace.22014","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).</p><p><strong>Methods: </strong>We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR.</p><p><strong>Results: </strong>In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76.</p><p><strong>Conclusions: </strong>Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.</p>","PeriodicalId":517436,"journal":{"name":"Annals of clinical epidemiology","volume":"4 4","pages":"110-119"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10760489/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and external validation of a deep learning-based computed tomography classification system for COVID-19.\",\"authors\":\"Yuki Kataoka, Tomohisa Baba, Tatsuyoshi Ikenoue, Yoshinori Matsuoka, Junichi Matsumoto, Junji Kumasawa, Kentaro Tochitani, Hiraku Funakoshi, Tomohiro Hosoda, Aiko Kugimiya, Michinori Shirano, Fumiko Hamabe, Sachiyo Iwata, Yoshiro Kitamura, Tsubasa Goto, Shingo Hamaguchi, Takafumi Haraguchi, Shungo Yamamoto, Hiromitsu Sumikawa, Koji Nishida, Haruka Nishida, Koichi Ariyoshi, Hiroaki Sugiura, Hidenori Nakagawa, Tomohiro Asaoka, Naofumi Yoshida, Rentaro Oda, Takashi Koyama, Yui Iwai, Yoshihiro Miyashita, Koya Okazaki, Kiminobu Tanizawa, Tomohiro Handa, Shoji Kido, Shingo Fukuma, Noriyuki Tomiyama, Toyohiro Hirai, Takashi Ogura\",\"doi\":\"10.37737/ace.22014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).</p><p><strong>Methods: </strong>We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR.</p><p><strong>Results: </strong>In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76.</p><p><strong>Conclusions: </strong>Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.</p>\",\"PeriodicalId\":517436,\"journal\":{\"name\":\"Annals of clinical epidemiology\",\"volume\":\"4 4\",\"pages\":\"110-119\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10760489/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of clinical epidemiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37737/ace.22014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of clinical epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37737/ace.22014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Development and external validation of a deep learning-based computed tomography classification system for COVID-19.
Background: We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).
Methods: We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR.
Results: In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76.
Conclusions: Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.
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