Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Saeed Reza  Motamedian; Negin  Cheraghi; Sadra  Mohaghegh; Elham Babadi  Oregani; Mahrsa  Amjadi; Parnian  Shobeiri; Niusha  Solouki; Nikoo  Ahmadi; Yassine  Bouchareb; Arman  Rahmim

doi:10.18502/fbt.v12i3.19190

Saeed Reza Motamedian Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
Negin Cheraghi Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Sadra Mohaghegh Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
Elham Babadi Oregani Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Mahrsa Amjadi Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Parnian Shobeiri Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
Niusha Solouki Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Nikoo Ahmadi Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Yassine Bouchareb Sultan Qaboos University, College of Medicine and Health Sciences, Radiology and Molecular Imaging, Muscat, PO Box 35, PC 123, Oman
Arman Rahmim Department of Radiology, University of British Columbia, Vancouver, BC, Canada

DOI: https://doi.org/10.18502/fbt.v12i3.19190

Keywords: Artificial Intelligence; Deep Learning; Machine Learning; COVID-19; Prognosis

Abstract

Purpose: Artificial Intelligence (AI) techniques have been extensively utilized for diagnosing and prognosing several diseases in recent years. This study identifies, appraises, and synthesizes published studies on the use of AI for the prognosis of COVID-19.

Materials and Methods: Electronic search was performed using Medline, Google Scholar, Scopus, Embase, Cochrane, and ProQuest. The systematic approach followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure comprehensive reporting. Studies that examined machine learning or deep learning methods to determine the prognosis of COVID-19 using Computed Tomography (CT) or chest X-Ray (CXR) images were included. Polled sensitivity, specificity, accuracy, Area Under the Curve (AUC), and diagnostic odds ratio were calculated.

Results: A total of 36 articles were included; various prognosis-related issues, including disease severity, mechanical ventilation, or admission to the intensive care unit, and mortality, were investigated. Several AI models and architectures were employed, such as the Siamense model, support vector machine, Random Forest, Extreme Gradient Boosting, and convolutional neural networks. The models achieved 71%, 88%, and 67% sensitivity for mortality, severity assessment, and need for ventilation, respectively. The specificities of 69%, 89%, and 89% were reported for the aforementioned variables.

Conclusion: Based on the included articles, machine learning and deep learning methods used for COVID-19 patients' prognosis using radiomic features from CT or CXR images can help clinicians manage patients and allocate resources more effectively. These studies also demonstrate that combining patient demographics, clinical data, laboratory tests, and radiomic features improves model performance.