Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis
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 perfo...
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2025-07-01
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| Series: | Frontiers in Biomedical Technologies |
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| Online Access: | https://fbt.tums.ac.ir/index.php/fbt/article/view/1103 |
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| author | Saeed Reza Motamedian Negin Cheraghi Sadra Mohaghegh Elham Babadi Oregani Mahrsa Amjadi Parnian Shobeiri Niusha Solouki Nikoo Ahmadi Yassine Bouchareb Arman Rahmim |
| author_facet | Saeed Reza Motamedian Negin Cheraghi Sadra Mohaghegh Elham Babadi Oregani Mahrsa Amjadi Parnian Shobeiri Niusha Solouki Nikoo Ahmadi Yassine Bouchareb Arman Rahmim |
| author_sort | Saeed Reza Motamedian |
| collection | DOAJ |
| description |
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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| format | Article |
| id | doaj-art-8467fc23a2ac4290ae92cd28ee9cd890 |
| institution | Kabale University |
| issn | 2345-5837 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Frontiers in Biomedical Technologies |
| spelling | doaj-art-8467fc23a2ac4290ae92cd28ee9cd8902025-08-20T03:41:10ZengTehran University of Medical SciencesFrontiers in Biomedical Technologies2345-58372025-07-0112310.18502/fbt.v12i3.19190Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysisSaeed Reza Motamedian0Negin Cheraghi1Sadra Mohaghegh2Elham Babadi Oregani3Mahrsa Amjadi4Parnian Shobeiri5Niusha Solouki6Nikoo Ahmadi7Yassine Bouchareb8Arman Rahmim9Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, GermanyDental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranTopic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, GermanyDental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranDental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranTopic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, GermanyDental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranDental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran3Sultan Qaboos University, College of Medicine and Health Sciences, Radiology and Molecular Imaging, Muscat, PO Box 35, PC 123, OmanDepartment of Radiology, University of British Columbia, Vancouver, BC, Canada 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. https://fbt.tums.ac.ir/index.php/fbt/article/view/1103Artificial IntelligenceDeep LearningMachine LearningCOVID-19Prognosis |
| spellingShingle | Saeed Reza Motamedian Negin Cheraghi Sadra Mohaghegh Elham Babadi Oregani Mahrsa Amjadi Parnian Shobeiri Niusha Solouki Nikoo Ahmadi Yassine Bouchareb Arman Rahmim Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis Frontiers in Biomedical Technologies Artificial Intelligence Deep Learning Machine Learning COVID-19 Prognosis |
| title | Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis |
| title_full | Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis |
| title_fullStr | Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis |
| title_full_unstemmed | Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis |
| title_short | Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis |
| title_sort | prognosis of covid 19 using artificial intelligence a systematic review and meta analysis |
| topic | Artificial Intelligence Deep Learning Machine Learning COVID-19 Prognosis |
| url | https://fbt.tums.ac.ir/index.php/fbt/article/view/1103 |
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