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06.01.2022 | COVID-19 | Imaging Informatics and Artificial Intelligence Zur Zeit gratis

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19

verfasst von: Hengrui Liang, Yuchen Guo, Xiangru Chen, Keng-Leong Ang, Yuwei He, Na Jiang, Qiang Du, Qingsi Zeng, Ligong Lu, Zebin Gao, Linduo Li, Quanzheng Li, Fangxing Nie, Guiguang Ding, Gao Huang, Ailan Chen, Yimin Li, Weijie Guan, Ling Sang, Yuanda Xu, Huai Chen, Zisheng Chen, Shiyue Li, Nuofu Zhang, Ying Chen, Danxia Huang, Run Li, Jianfu Li, Bo Cheng, Yi Zhao, Caichen Li, Shan Xiong, Runchen Wang, Jun Liu, Wei Wang, Jun Huang, Fei Cui, Tao Xu, Fleming Y. M. Lure, Meixiao Zhan, Yuanyi Huang, Qiang Yang, Qionghai Dai, Wenhua Liang, Jianxing He, Nanshan Zhong

Erschienen in: European Radiology | Ausgabe 4/2022

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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.

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Titel: Artificial intelligence for stepwise diagnosis and monitoring of COVID-19
verfasst von: Hengrui Liang
Yuchen Guo
Xiangru Chen
Keng-Leong Ang
Yuwei He
Na Jiang
Qiang Du
Qingsi Zeng
Ligong Lu
Zebin Gao
Linduo Li
Quanzheng Li
Fangxing Nie
Guiguang Ding
Gao Huang
Ailan Chen
Yimin Li
Weijie Guan
Ling Sang
Yuanda Xu
Huai Chen
Zisheng Chen
Shiyue Li
Nuofu Zhang
Ying Chen
Danxia Huang
Run Li
Jianfu Li
Bo Cheng
Yi Zhao
Caichen Li
Shan Xiong
Runchen Wang
Jun Liu
Wei Wang
Jun Huang
Fei Cui
Tao Xu
Fleming Y. M. Lure
Meixiao Zhan
Yuanyi Huang
Qiang Yang
Qionghai Dai
Wenhua Liang
Jianxing He
Nanshan Zhong
Publikationsdatum: 06.01.2022
Verlag: Springer Berlin Heidelberg
Schlagwort: COVID-19
Erschienen in: European Radiology / Ausgabe 4/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-08334-6

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