Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees
Introduction: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. Methodology: DL dia...
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The Journal of Infection in Developing Countries
2022-11-01
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| Series: | Journal of Infection in Developing Countries |
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| Online Access: | https://jidc.org/index.php/journal/article/view/15022 |
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| author | Dawei Dong Zujin Luo Yue Zheng Ying Liang Pengfei Zhao Linlin Feng Dawei Wang Ying Cao Zhenhao Zhao Yingmin Ma |
| author_facet | Dawei Dong Zujin Luo Yue Zheng Ying Liang Pengfei Zhao Linlin Feng Dawei Wang Ying Cao Zhenhao Zhao Yingmin Ma |
| author_sort | Dawei Dong |
| collection | DOAJ |
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Introduction: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail.
Methodology: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society.
Results: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features.
Conclusions: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well.
Advances in knowledge: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.
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| format | Article |
| id | doaj-art-e56251e4a0974d3faa55ae570345338c |
| institution | OA Journals |
| issn | 1972-2680 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | The Journal of Infection in Developing Countries |
| record_format | Article |
| series | Journal of Infection in Developing Countries |
| spelling | doaj-art-e56251e4a0974d3faa55ae570345338c2025-08-20T02:16:14ZengThe Journal of Infection in Developing CountriesJournal of Infection in Developing Countries1972-26802022-11-01161110.3855/jidc.15022Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returneesDawei Dong0Zujin Luo1Yue Zheng2Ying Liang3Pengfei Zhao4Linlin Feng5Dawei Wang6Ying Cao7Zhenhao Zhao8Yingmin Ma9Department of Radiology, Beijing Xiaotangshan Hospital, Beijing, ChinaDepartment of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaIntensive Care Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, ChinaDepartment of Radiology, Beijing Xiaotangshan Hospital, Beijing, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaDepartment of Radiology, Beijing Xiaotangshan Hospital, Beijing, ChinaInstitute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, ChinaInstitute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, ChinaInstitute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, ChinaDepartment of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China Introduction: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. Methodology: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. Results: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features. Conclusions: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. Advances in knowledge: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases. https://jidc.org/index.php/journal/article/view/15022Deep learningdiagnostic systemsperformance evaluationCOVID-19asymptomatic cases |
| spellingShingle | Dawei Dong Zujin Luo Yue Zheng Ying Liang Pengfei Zhao Linlin Feng Dawei Wang Ying Cao Zhenhao Zhao Yingmin Ma Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees Journal of Infection in Developing Countries Deep learning diagnostic systems performance evaluation COVID-19 asymptomatic cases |
| title | Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees |
| title_full | Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees |
| title_fullStr | Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees |
| title_full_unstemmed | Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees |
| title_short | Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees |
| title_sort | application of deep learning based diagnostic systems in screening asymptomatic covid 19 patients among oversea returnees |
| topic | Deep learning diagnostic systems performance evaluation COVID-19 asymptomatic cases |
| url | https://jidc.org/index.php/journal/article/view/15022 |
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