3 documents found, page 1 of 1
AI for the hemodynamic assessment of critically ill and surgical patients: focu...
Michard, Frederic; Mulder, Marijn P.; Gonzalez, Filipe; Sanfilippo, FilippoSeveral artificial intelligence (AI)-driven tools have emerged for the hemodynamic evaluation of critically ill and surgical patients. This article provides an overview of current developments and potential clinical applications of machine learning (ML) for blood pressure measurements, hypotension prediction, hemodynamic profiling, and echocardiography. ML algorithms have shown promise in enabling continuous, n...
Machine learning for the real-time assessment of left ventricular ejection frac...
Varudo, Rita; Gonzalez, Filipe; Leote, João; Martins, Cristina; Bacariza, Jacobo; Fernandes, Antero; Michard, FredericBackground: Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. Methods: Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken...
Automation of sub-aortic velocity time integral measurements by transthoracic e...
Gonzalez, Filipe; Varudo, Rita; Leote, João; Martins, Cristina; Bacariza, Jacobo; Fernandes, Antero; Michard, FredericPoint-of-care ultrasound techniques are increasingly used for the bedside assessment of cardiac function and haemodynamics in critically ill patients. The sub-aortic or left ventricular outflow tract velocity time integral (VTI) can be measured using pulsed-Doppler ultrasonography from a transthoracic apical 5-chamber view. Quantifying VTI is useful to discriminate between vasoplegic states (hypotension with no...