Author: Mohamed Ali, Abdel-Moneim; El-Alali, Emran; Weltz, Adam S; Rehrig, Scott T
Title: Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning. Cord-id: mv0xbkko Document date: 2021_5_7
ID: mv0xbkko
Snippet: 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 ma
Document: 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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