Chest CT Images for COVID-19: Radiologists and Computer-Based Detection
BackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from othe...
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| Format: | Article |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2021.614207/full |
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| author | Qingli Dou Jiangping Liu Wenwu Zhang Yanan Gu Wan-Ting Hsu Kuan-Ching Ho Hoi Sin Tong Wing Yan Yu Chien-Chang Lee |
| author_facet | Qingli Dou Jiangping Liu Wenwu Zhang Yanan Gu Wan-Ting Hsu Kuan-Ching Ho Hoi Sin Tong Wing Yan Yu Chien-Chang Lee |
| author_sort | Qingli Dou |
| collection | DOAJ |
| description | BackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation. |
| first_indexed | 2024-12-17T22:10:38Z |
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| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-17T22:10:38Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-a8fbdc6e1441431cbdc6302fd3c94efd2022-12-21T21:30:45ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2021-03-01810.3389/fmolb.2021.614207614207Chest CT Images for COVID-19: Radiologists and Computer-Based DetectionQingli Dou0Jiangping Liu1Wenwu Zhang2Yanan Gu3Wan-Ting Hsu4Kuan-Ching Ho5Hoi Sin Tong6Wing Yan Yu7Chien-Chang Lee8Department of Emergency Medicine, The Second Affiliated Hospital of Shenzhen University, Shenzhen, ChinaDepartment of Emergency Medicine, The Second Affiliated Hospital of Shenzhen University, Shenzhen, ChinaDepartment of Emergency Medicine, The Second Affiliated Hospital of Shenzhen University, Shenzhen, ChinaDepartment of Emergency Medicine, The Second Affiliated Hospital of Shenzhen University, Shenzhen, ChinaDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United StatesRadiology Department, St George Hospital Sydney, Kogarah, NSW, AustraliaLi Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong KongLi Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong KongDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, TaiwanBackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.https://www.frontiersin.org/articles/10.3389/fmolb.2021.614207/fullCOVID-192019-nCoVchest computed tomographycomputer-aided detectioncomputer-based detection |
| spellingShingle | Qingli Dou Jiangping Liu Wenwu Zhang Yanan Gu Wan-Ting Hsu Kuan-Ching Ho Hoi Sin Tong Wing Yan Yu Chien-Chang Lee Chest CT Images for COVID-19: Radiologists and Computer-Based Detection Frontiers in Molecular Biosciences COVID-19 2019-nCoV chest computed tomography computer-aided detection computer-based detection |
| title | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
| title_full | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
| title_fullStr | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
| title_full_unstemmed | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
| title_short | Chest CT Images for COVID-19: Radiologists and Computer-Based Detection |
| title_sort | chest ct images for covid 19 radiologists and computer based detection |
| topic | COVID-19 2019-nCoV chest computed tomography computer-aided detection computer-based detection |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2021.614207/full |
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