Artificial Intelligence-Based Assessment of Left Ventricular Filling Pressures From 2-Dimensional Cardiac Ultrasound Images.
The estimation of left ventricular (LV) filling pressure from the ratio of transmitral and annular velocities (E/e0 ) is used commonly for identifying diastolic dysfunction in patients who complain of exertional dyspnea (1). We have recently illustrated that LV and left atrial speckle tracking echocardiography (STE)- derived measurements have similar information content as do conventional 2-dimensional and Doppler methods for characterizing LV diastolic dysfunction (2). Therefore, an alternative approach could be to use an automated approach in which myocardial deformation variables with machine learning (ML) models deliver a rapid decision support system from just 2-dimensional cardiac ultrasound images for deriving the same level of information regarding left ventricular filling pressures (LVFP) as provided by E/e0 .
We explored the development and validation of an ML model for assessing LVFP in 174 patients. The details regarding these subjects have been previously described (2). The study sample size was split into an ML training group of 130 patients (75%) and an ML testing group of 44 (25%) who also had the pulmonary capillary wedge pressure invasively measured using right cardiac catheterization. Patients were classified as elevated or reduced LVFP, as suggested by the echocardiographic ratio between early diastolic mitral flow velocities to early diastolic mitral annular velocity averaged from the septal and lateral positions (E/e0 ) $13. An ensemble model of ML algorithms was then applied to the STE data for the prediction of elevated LVFP. The models output in the testing sets were also verified for identification of elevated pulmonary capillary wedge pressure ($18 mm Hg).Full PDF Version