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Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning.
Mohamed Ali, Abdel-Moneim; El-Alali, Emran; Weltz, Adam S; Rehrig, Scott T.
  • Mohamed Ali AM; Department of Surgery, 1267Anne Arundel Medical Center, Annapolis, MD, USA.
  • El-Alali E; Department of Internal Medicine, 1267Anne Arundel Medical Center, Annapolis, MD, USA.
  • Weltz AS; Department of Surgery, 1267Anne Arundel Medical Center, Annapolis, MD, USA.
  • Rehrig ST; Department of Surgery, 1267Anne Arundel Medical Center, Annapolis, MD, USA.
Surg Innov ; 28(2): 214-219, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1219686
ABSTRACT
Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Ultrasonography / Point-of-Care Systems / Machine Learning / COVID-19 Type of study: Prognostic study Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Surg Innov Year: 2021 Document Type: Article Affiliation country: 15533506211018671

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Ultrasonography / Point-of-Care Systems / Machine Learning / COVID-19 Type of study: Prognostic study Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Surg Innov Year: 2021 Document Type: Article Affiliation country: 15533506211018671