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
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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31- L. Wang et al., "Artificial Intelligence for COVID-19: A Systematic Review." (in eng), Front Med (Lausanne), Vol. 8p. 704256, (2021).
32- Jawad Rasheed et al., "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic." Chaos, Solitons & Fractals, Vol. 141p. 110337, (2020).
33- Matthew DF McInnes et al., "Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement." Jama, Vol. 319 (No. 4), pp. 388-96, (2018).
34- Simar Singh Bajaj, Alister Francois Martin, and Fatima Cody Stanford, "Health-based civic engagement is a professional responsibility." Nature Medicine, Vol. 27 (No. 10), pp. 1661-63, (2021).
35- Soroush Sadr et al., "Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy." Journal of Endodontics, 2022/12/21/ (2022).
36- Hossein Mohammad-Rahimi et al., "Deep learning in periodontology and oral implantology: A scoping review." Journal of Periodontal Research, Vol. 57 (No. 5), pp. 942-51, (2022).
37- Hossein Mohammad-Rahimi et al., "Deep learning for caries detection: A systematic review." Journal of Dentistry, Vol. 122p. 104115, 2022/07/01/ (2022).
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| Issue | Articles in Press | |
| Section | Systematic Review(s) | |
| Keywords | ||
| Artificial Intelligence Deep Learning Machine Learning COVID-19 Prognosis | ||
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How to Cite
1.
Motamedian SR, Cheraghi N, Mohaghegh S, Babadi Oregani E, Amjadi M, Shobeiri P, Solouki N, Ahmadi N, Bouchareb Y, Rahmim A. Prognosis of COVID-19 using Artificial Intelligence: A Systematic Review and Meta-analysis. Frontiers Biomed Technol. 2025;.
