*In Applied sciences (Basel, Switzerland) *

^{2}values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.

*Damen Frederick W, Newton David T, Lin Guang, Goergen Craig J*

*2021-Feb-02*

**4D ultrasound, boundary prediction, cardiac kinematics, echocardiography, hypertrophic cardiomyopathy, left ventricle, machine learning, murine, myocardium, volumetric imaging**