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A Comprehensive Review of Machine Learning Used to Combat COVID-19
| Content Provider | MDPI |
|---|---|
| Author | Gomes, Rahul Kamrowski, Connor Langlois, Jordan Rozario, Papia Dircks, Ian Grottodden, Keegan Martinez, Matthew Tee, Wei Zhong Sargeant, Kyle LaFleur, Corbin Haley, Mitchell |
| Copyright Year | 2022 |
| Description | Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19. |
| Starting Page | 1853 |
| e-ISSN | 20754418 |
| DOI | 10.3390/diagnostics12081853 |
| Journal | Diagnostics |
| Issue Number | 8 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-31 |
| Access Restriction | Open |
| Subject Keyword | Diagnostics Medical Informatics Covid-19 Prognosis Deep Learning Machine Learning Ct Scan X-rays |
| Content Type | Text |
| Resource Type | Article |
