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Eur Radiol
. 2022 Jan 6.
doi: 10.1007/s00330-021-08334-6. Online ahead of print.
Artificial intelligence for stepwise diagnosis and monitoring of COVID-19
Hengrui Liang # 1 2 , Yuchen Guo # 3 4 , Xiangru Chen 5 , Keng-Leong Ang 2 6 , Yuwei He 4 7 , Na Jiang 8 , Qiang Du 5 , Qingsi Zeng 1 9 , Ligong Lu 10 , Zebin Gao 5 , Linduo Li 11 , Quanzheng Li 12 , Fangxing Nie 5 , Guiguang Ding 3 4 7 , Gao Huang 5 7 , Ailan Chen 1 13 , Yimin Li 1 14 , Weijie Guan 1 , Ling Sang 1 14 , Yuanda Xu 1 14 , Huai Chen 1 9 , Zisheng Chen 1 , Shiyue Li 1 , Nuofu Zhang 1 , Ying Chen 1 , Danxia Huang 1 , Run Li 1 , Jianfu Li 1 2 , Bo Cheng 1 2 , Yi Zhao 1 2 , Caichen Li 1 2 , Shan Xiong 1 2 , Runchen Wang 1 2 , Jun Liu 1 2 , Wei Wang 1 2 , Jun Huang 1 2 , Fei Cui 1 2 , Tao Xu 15 , Fleming Y M Lure 16 , Meixiao Zhan 10 , Yuanyi Huang 17 , Qiang Yang 18 , Qionghai Dai 19 20 , Wenhua Liang 21 22 , Jianxing He 23 24 25 , Nanshan Zhong 1
Affiliations
- PMID: 34988656
- DOI: 10.1007/s00330-021-08334-6
Abstract
Background: 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.
Keywords: AI (artificial intelligence); Computer-assisted diagnosis; Coronavirus disease 2019.