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.org2023, 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.org2023, 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.
Commenter: Mohammad Arafat Hussain
Commenter's Conflict of Interests: Author