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dc.contributor.authorAng, Keng-Leong
dc.date.accessioned2022-01-28T12:09:29Z
dc.date.available2022-01-28T12:09:29Z
dc.date.issued2022
dc.identifier.citationLiang, H., Guo, Y., Chen, X., Ang, K. L., He, Y., Jiang, N., Du, Q., Zeng, Q., Lu, L., Gao, Z., Li, L., Li, Q., Nie, F., Ding, G., Huang, G., Chen, A., Li, Y., Guan, W., Sang, L., Xu, Y., … Zhong, N. (2022). Artificial intelligence for stepwise diagnosis and monitoring of COVID-19. European radiology, 1–11. Advance online publication. https://doi.org/10.1007/s00330-021-08334-6en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12904/15117
dc.description.abstractBackground: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. Methods: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. Results: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. Interpretation: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. Key points: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.
dc.description.urihttps://link.springer.com/article/10.1007%2Fs00330-021-08334-6en_US
dc.language.isoenen_US
dc.subjectartificial intelligenceen_US
dc.subjectcomputer-assisted diagnosisen_US
dc.subjectCOVID-19en_US
dc.titleArtificial intelligence for stepwise diagnosis and monitoring of COVID-19en_US
dc.typeArticleen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.versionVoRen_US
rioxxterms.versionofrecordhttps://doi.org/10.1007/s00330-021-08334-6en_US
rioxxterms.typeJournal Article/Reviewen_US
refterms.dateFCD2022-01-28T12:09:30Z
refterms.versionFCDVoR
refterms.dateFOA2022-01-28T12:09:30Z
refterms.panelUnspecifieden_US
refterms.dateFirstOnline2022-01
html.description.abstractBackground: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. Methods: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. Results: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. Interpretation: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. Key points: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.en_US
rioxxterms.funder.project94a427429a5bcfef7dd04c33360d80cden_US


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