Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

Version 1 : Received: 15 March 2023 / Approved: 16 March 2023 / Online: 16 March 2023 (02:53:04 CET)
Version 2 : Received: 21 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (03:42:44 CEST)
Version 3 : Received: 23 May 2023 / Approved: 24 May 2023 / Online: 25 May 2023 (02:41:35 CEST)

How to cite: Morshed, A.; Al Shihab, A.; Jahin, M.A.; Al Nahian, M.J.; Sarker, M.M.H.; Ibne Wadud, M.S.; Uddin, M.I.; Siraji, M.I.; Anjum, N.; Shristy, S.R.; Rahman, T.; Khatun, M.; Dewan, M.R.; Hossain, M.; Sultana, R.; Chakma, R.; Emon, S.B.; Islam, T.; Hussain, M. Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies. Preprints.org 2023, 2023030296. https://doi.org/10.20944/preprints202303.0296.v3 Morshed, A.; Al Shihab, A.; Jahin, M.A.; Al Nahian, M.J.; Sarker, M.M.H.; Ibne Wadud, M.S.; Uddin, M.I.; Siraji, M.I.; Anjum, N.; Shristy, S.R.; Rahman, T.; Khatun, M.; Dewan, M.R.; Hossain, M.; Sultana, R.; Chakma, R.; Emon, S.B.; Islam, T.; Hussain, M. Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies. Preprints.org 2023, 2023030296. https://doi.org/10.20944/preprints202303.0296.v3

Cite as:

Morshed, A.; Al Shihab, A.; Jahin, M.A.; Al Nahian, M.J.; Sarker, M.M.H.; Ibne Wadud, M.S.; Uddin, M.I.; Siraji, M.I.; Anjum, N.; Shristy, S.R.; Rahman, T.; Khatun, M.; Dewan, M.R.; Hossain, M.; Sultana, R.; Chakma, R.; Emon, S.B.; Islam, T.; Hussain, M. Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies. Preprints.org 2023, 2023030296. https://doi.org/10.20944/preprints202303.0296.v3 Morshed, A.; Al Shihab, A.; Jahin, M.A.; Al Nahian, M.J.; Sarker, M.M.H.; Ibne Wadud, M.S.; Uddin, M.I.; Siraji, M.I.; Anjum, N.; Shristy, S.R.; Rahman, T.; Khatun, M.; Dewan, M.R.; Hossain, M.; Sultana, R.; Chakma, R.; Emon, S.B.; Islam, T.; Hussain, M. Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies. Preprints.org 2023, 2023030296. https://doi.org/10.20944/preprints202303.0296.v3

Abstract

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

Keywords

COVID-19; Deep learning; Artificial Intelligence; Ultrasound, Review

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Comments (1)

Comment 1

Received: 25 May 2023
Commenter: Mohammad Arafat Hussain
Commenter's Conflict of Interests: Author

Comment: In version 2, we made a mistake in the author list and author order. We have fixed it in the latest version.

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