| Autor: |
Liang, Hengrui, Guo, Yuchen, Chen, Xiangru, Ang, Keng-Leong, He, Yuwei, Jiang, Na, Du, Qiang, Zeng, Qingsi, Lu, Ligong, Gao, Zebin, Li, Linduo, Li, Quanzheng, Nie, Fangxing, Ding, Guiguang, Huang, Gao, Chen, Ailan, Li, Yimin, Guan, Weijie, Sang, Ling, Xu, Yuanda, Chen, Huai, Chen, Zisheng, Li, Shiyue, Zhang, Nuofu, Chen, Ying, Huang, Danxia, Li, Run, Li, Jianfu, Cheng, Bo, Zhao, Yi, Li, Caichen, Xiong, Shan, Wang, Runchen, Liu, Jun, Wang, Wei, Huang, Jun, Cui, Fei, Xu, Tao, Lure, Fleming Y. M., Zhan, Meixiao, Huang, Yuanyi, Yang, Qiang, Dai, Qionghai, Liang, Wenhua, He, Jianxing, Zhong, Nanshan |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
European Radiology |
| ISSN: |
1432-1084 |
| Popis: |
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. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08334-6. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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