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Review
. 2022 Jul 31;12(8):1853.
doi: 10.3390/diagnostics12081853.

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Rahul Gomes  1 , Connor Kamrowski  1 , Jordan Langlois  1 , Papia Rozario  2 , Ian Dircks  1 , Keegan Grottodden  1 , Matthew Martinez  1 , Wei Zhong Tee  1 , Kyle Sargeant  1 , Corbin LaFleur  1 , Mitchell Haley  1
Affiliations
Review

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Rahul Gomes et al. Diagnostics (Basel). .

Abstract

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.

Keywords: COVID-19 prognosis; CT scan; X-rays; deep learning; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Research presented in this paper summarized based on (a) country and (b) publisher.
Figure 2
Figure 2
Support vectors on yellow area, are used to create boundaries also known as hyperplanes. The SVM in the figure is using a linear kernel denoted by the straight line equation. Image distributed under a CC BY-SA 4.0 license.
Figure 3
Figure 3
The structure of DNN model. Image distributed under a CC BY-SA 4.0 license. Number one represents input layer followed by two which are the hidden layers and finally three which is the output layer.
Figure 4
Figure 4
The structure of a UNet model similar to the original one proposed in [17]. Image distributed under a CC BY-SA 4.0 license.
Figure 5
Figure 5
The structure of VGG-16 model adapted for prediction of COVID-19 using CXR.
Figure 6
Figure 6
The structure of ResNet 50 model [19], distributed under a CC BY-SA 4.0 license.
Figure 7
Figure 7
A visual interpretation of (A) standard convolution compared to (B) depthwise separable convolutions used in MobileNet.
Figure 8
Figure 8
The structure of DenseNet [21] block shown in [22]. Features are added from all preceding blocks.
Figure 9
Figure 9
The structure of an LSTM block used to process longitudinal data and distributed under a CC BY-SA 4.0 license.
Figure 10
Figure 10
Common deep learning architectures explored in COVID-19 diagnosis.
Figure 11
Figure 11
Papers reviewed in this research presented based on the three primary objectives.
See this image and copyright information in PMC

References

    1. Feng W., Newbigging A.M., Le C., Pang B., Peng H., Cao Y., Wu J., Abbas G., Song J., Wang D.B., et al. Molecular diagnosis of COVID-19: Challenges and research needs. Anal. Chem. 2020;92:10196–10209. doi: 10.1021/acs.analchem.0c02060. - DOI - PubMed
    1. Kanne J.P., Little B.P., Chung J.H., Elicker B.M., Ketai L.H. Essentials for radiologists on COVID-19: An update—Radiology scientific expert panel. Radiology. 2020;296:E113–E114. doi: 10.1148/radiol.2020200527. - DOI - PMC - PubMed
    1. Sousa R.T., Marques O., Soares F.A.A., Sene I.I., Jr., de Oliveira L.L., Spoto E.S. Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs. Procedia Comput. Sci. 2013;18:2579–2582. doi: 10.1016/j.procs.2013.05.444. - DOI
    1. Ahsan M., Gomes R., Denton A. Application of a convolutional neural network using transfer learning for tuberculosis detection; Proceedings of the 2019 IEEE International Conference on Electro Information Technology (EIT); Brookings, SD, USA. 20–22 May 2019; pp. 427–433.
    1. Bentley P., Ganesalingam J., Jones A.L.C., Mahady K., Epton S., Rinne P., Sharma P., Halse O., Mehta A., Rueckert D. Prediction of stroke thrombolysis outcome using CT brain machine learning. NeuroImage Clin. 2014;4:635–640. doi: 10.1016/j.nicl.2014.02.003. - DOI - PMC - PubMed