Skip to main content

SYSTEMATIC REVIEW article

Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1371852
This article is part of the Research Topic Single and Multi-Pathogen Epidemiology and Control View all 8 articles

Performance of artificial intelligence in predicting the prognosis of severe COVID-19: a systematic review and meta-analysis

Provisionally accepted
Chu Qin Chu Qin 1Huan Ma Huan Ma 1Mahong Hu Mahong Hu 2Xiujuan Xu Xiujuan Xu 2*Conghua Ji Conghua Ji 1*
  • 1 Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
  • 2 Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    Background: COVID-19-induced pneumonia has become a persistent health concern, with severe cases posing a significant threat to patient lives. However, the potential of artificial intelligence (AI) in assisting physicians in predicting the prognosis of severe COVID-19 patients remains unclear.To obtain relevant studies, two researchers conducted a comprehensive search of the PubMed, Web of Science, and Embase databases, including all studies published up to October 31, 2023, that utilized AI to predict mortality rates in severe COVID-19 patients. The PROBAST 2019 tool was employed to assess the potential bias in the included studies, and Stata 16 was used for meta-analysis, publication bias assessment, and sensitivity analysis.Results: A total of 19 studies, comprising 26 models, were included in the analysis.Among them, the models that incorporated both clinical and radiological data demonstrated the highest performance. These models achieved an overall sensitivity of 0.81 (0.64-0.91), specificity of 0.77 (0.71-0.82), and an overall area under the curve (AUC) of 0.88 (0.85-0.90). Subgroup analysis revealed notable findings. Studies conducted in developed countries exhibited significantly higher predictive specificity for both radiological and combined models (p<0.05). Additionally, investigations involving 2 non-intensive care unit patients demonstrated significantly greater predictive specificity (p<0.001).The current evidence suggests that artificial intelligence prediction models show promising performance in predicting the prognosis of severe COVID-19 patients.However, due to variations in the suitability of different models for specific populations, it is not yet certain whether they can be fully applied in clinical practice. There is still room for improvement in their predictive capabilities, and future research and development efforts are needed.

    Keywords: artificial intelligence, COVID-19, Mortality, Systematic reviews, Metaanalyses

    Received: 17 Jan 2024; Accepted: 18 Jul 2024.

    Copyright: © 2024 Qin, Ma, Hu, Xu and Ji. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xiujuan Xu, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, Zhejiang Province, China
    Conghua Ji, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.