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Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic.
Wang, Jing; Yang, Xiaofeng; Zhou, Boran; Sohn, James J; Zhou, Jun; Jacob, Jesse T; Higgins, Kristin A; Bradley, Jeffrey D; Liu, Tian.
  • Wang J; Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA.
  • Yang X; Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA.
  • Zhou B; Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA.
  • Sohn JJ; Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23219, USA.
  • Zhou J; Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA.
  • Jacob JT; Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, GA 30322, USA.
  • Higgins KA; Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA.
  • Bradley JD; Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA.
  • Liu T; Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA.
J Imaging ; 8(3)2022 Mar 05.
Article in English | MEDLINE | ID: covidwho-1732094
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Reviews / Systematic review/Meta Analysis Language: English Year: 2022 Document Type: Article Affiliation country: Jimaging8030065

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Reviews / Systematic review/Meta Analysis Language: English Year: 2022 Document Type: Article Affiliation country: Jimaging8030065