Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic
Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its un...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/8/3/65 |
| _version_ | 1797470380010504192 |
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| author | Jing Wang Xiaofeng Yang Boran Zhou James J. Sohn Jun Zhou Jesse T. Jacob Kristin A. Higgins Jeffrey D. Bradley Tian Liu |
| author_facet | Jing Wang Xiaofeng Yang Boran Zhou James J. Sohn Jun Zhou Jesse T. Jacob Kristin A. Higgins Jeffrey D. Bradley Tian Liu |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques. |
| first_indexed | 2024-03-09T19:35:44Z |
| format | Article |
| id | doaj.art-8fb30d3eb6a74e28b8dc1217514de5c0 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T19:35:44Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-8fb30d3eb6a74e28b8dc1217514de5c02023-11-24T01:54:58ZengMDPI AGJournal of Imaging2313-433X2022-03-01836510.3390/jimaging8030065Review of Machine Learning in Lung Ultrasound in COVID-19 PandemicJing Wang0Xiaofeng Yang1Boran Zhou2James J. Sohn3Jun Zhou4Jesse T. Jacob5Kristin A. Higgins6Jeffrey D. Bradley7Tian Liu8Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USADepartment of Radiation Oncology, Emory University, Atlanta, GA 30322, USADepartment of Radiation Oncology, Emory University, Atlanta, GA 30322, USADepartment of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23219, USADepartment of Radiation Oncology, Emory University, Atlanta, GA 30322, USADivision of Infectious Diseases, Department of Medicine, Emory University, Atlanta, GA 30322, USADepartment of Radiation Oncology, Emory University, Atlanta, GA 30322, USADepartment of Radiation Oncology, Emory University, Atlanta, GA 30322, USADepartment of Radiation Oncology, Emory University, Atlanta, GA 30322, USAUltrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.https://www.mdpi.com/2313-433X/8/3/65lung ultrasoundmachine learningdeep learningCOVID-19artificial intelligence (AI) |
| spellingShingle | Jing Wang Xiaofeng Yang Boran Zhou James J. Sohn Jun Zhou Jesse T. Jacob Kristin A. Higgins Jeffrey D. Bradley Tian Liu Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic Journal of Imaging lung ultrasound machine learning deep learning COVID-19 artificial intelligence (AI) |
| title | Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic |
| title_full | Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic |
| title_fullStr | Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic |
| title_full_unstemmed | Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic |
| title_short | Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic |
| title_sort | review of machine learning in lung ultrasound in covid 19 pandemic |
| topic | lung ultrasound machine learning deep learning COVID-19 artificial intelligence (AI) |
| url | https://www.mdpi.com/2313-433X/8/3/65 |
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