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. 2021 Mar 25:8:638011.
doi: 10.3389/fcvm.2021.638011. eCollection 2021.

Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

Hossein Mohammad-Rahimi  1 , Mohadeseh Nadimi  2   3 , Azadeh Ghalyanchi-Langeroudi  2   3 , Mohammad Taheri  4 , Soudeh Ghafouri-Fard  5
Affiliations

Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

Hossein Mohammad-Rahimi et al. Front Cardiovasc Med. .

Abstract

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.

Keywords: COVID-19; X-ray image; biomarker; detection; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) chart showing the process of systematic identification, screening, and selection of articles.
See this image and copyright information in PMC

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