Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point‐of‐Care Ultrasound?
9
Zitationen
5
Autoren
2022
Jahr
Abstract
Performance of a DL model trained on formal echocardiography worsened when challenged with images captured during resuscitations. DL models intended for assessing bedside ultrasound should be trained on datasets composed of POCUS images. Such datasets have yet to be made publicly available.
Ähnliche Arbeiten
Recommendations regarding quantitation in M-mode echocardiography: results of a survey of echocardiographic measurements.
1978 · 7.465 Zit.
2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS)
2019 · 4.555 Zit.
International evidence-based recommendations for point-of-care lung ultrasound
2012 · 2.815 Zit.
Value of the Ventilation/Perfusion Scan in Acute Pulmonary Embolism
1990 · 2.736 Zit.
Guidelines for Performing a Comprehensive Transthoracic Echocardiographic Examination in Adults: Recommendations from the American Society of Echocardiography
2018 · 2.403 Zit.