Prognosis of COVID-19 using Artificial Intelligence: A Systematic Review and Meta-analysis

• URL: http://arxiv.org/abs/2408.00208v1
• Date: Thu, 1 Aug 2024 00:33:32 GMT
• Title: Prognosis of COVID-19 using Artificial Intelligence: A Systematic Review and Meta-analysis
• Authors: SaeedReza Motamedian, Sadra Mohaghegh, Elham Babadi Oregani, Mahrsa Amjadi, Parnian Shobeiri, Negin Cheraghi, Niusha Solouki, Nikoo Ahmadi, Hossein Mohammad-Rahimi, Yassine Bouchareb, Arman Rahmim,
• Abstract summary: This study identifies, appraises and synthesizes published studies on the use of AI for the prognosis of COVID-19. 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.
• Score: 0.23221087157793407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Artificial intelligence (AI) techniques have been extensively utilized for diagnosing and prognosis of several diseases in recent years. This study identifies, appraises and synthesizes published studies on the use of AI for the prognosis of COVID-19. Method: Electronic search was performed using Medline, Google Scholar, Scopus, Embase, Cochrane and ProQuest. Studies that examined machine learning or deep learning methods to determine the prognosis of COVID-19 using CT or chest X-ray images were included. Polled sensitivity, specificity area under the curve and diagnostic odds ratio were calculated. Result: 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 specificity 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 the prognosis of COVID-19 patients 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 demographic, clinical data, laboratory tests and radiomic features improves model performances.

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