iScience
Volume 25, Issue 5, 20 May 2022, 104227
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Article
The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system

https://doi.org/10.1016/j.isci.2022.104227Get rights and content
Under a Creative Commons license
open access

Highlights

  • COVID-19 patients with 341 longitudinal CT scans and paired clinical data included

  • A new AI model for the prediction of COVID-19 progression was developed

  • CT scans show significant add-on value over clinical data for the prediction

  • Day 6–8 after the onset of COVID-19 symptoms is an ideal time window for a CT scan

Summary

The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.

Subject areas

health sciences
microbiology
artificial intelligence
machine learning

Data and code availability

  • Partial data reported in this paper will be shared by the lead contact upon request. The longitudinal data reported in this study cannot be deposited in a public repository because patients do not want their data to be made public.

  • All original code has been deposited at https://robin970822.github.io/DABC-Net-for-COVID-19/and is publicly available as of the date of publication.

  • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Cited by (0)

10

Lead contact

11

These authors contributed equally