Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis

Tahmina Nasrin Poly; Md Mohaimenul Islam; Yu-Chuan Jack Li; Belal Alsinglawi; Min-Huei Hsu; Wen Shan Jian; Hsuan-Chia Yang

doi:10.2196/21394

Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis

JMIR Med Inform. 2021 Apr 29;9(4):e21394. doi: 10.2196/21394.

Authors

Tahmina Nasrin Poly^{1

2

3}, Md Mohaimenul Islam^{1

2

3}, Yu-Chuan Jack Li^{1

2

3

4

5}, Belal Alsinglawi⁶, Min-Huei Hsu⁷, Wen Shan Jian⁸, Hsuan-Chia Yang^{1

2

3}

Affiliations

¹ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
² International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.
³ Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
⁴ Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
⁵ TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan.
⁶ School of Computer, Data, and Mathematical Science, Western Sydney University, Sydney, Australia.
⁷ Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan.
⁸ School of Health Care Administration, Taipei Medical University, Taipei, Taiwan.

PMID: 33764884
PMCID: PMC8086786
DOI: 10.2196/21394

Abstract

Background: The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia.

Objective: The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images.

Methods: A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms "COVID-19," or "coronavirus," or "SARS-CoV-2," or "novel corona," or "2019-ncov," and "deep learning," or "artificial intelligence," or "automatic detection." Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus.

Results: A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools.

Conclusions: Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Keywords: COVID-19; SARS-CoV-2; artificial intelligence; deep learning; pneumonia.

©Tahmina Nasrin Poly, Md Mohaimenul Islam, Yu-Chuan Jack Li, Belal Alsinglawi, Min-Huei Hsu, Wen Shan Jian, Hsuan-Chia Yang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 29.04.2021.