Inter-observer and Inter-vendor Variability in Strain Measurements in Patients with Single Right Ventricular Anatomy

The mechanics of congenital heart disease: from a morphological trait to the functional echocardiographic evaluation

Advances in pediatric cardiac surgery have resulted in a recent growing epidemic of children and young adults with congenital heart diseases (CHDs). In these patients, congenital defects themselves, surgical operations and remaining lesions may alter cardiac anatomy and impact the mechanical performance of both ventricles. Cardiac function significantly influences outcomes in CHDs, necessitating regular patient follow-up to detect clinical changes and relevant risk factors. Echocardiography remains the primary imaging method for CHDs, but clinicians must understand patients' unique anatomies as different CHDs exhibit distinct anatomical characteristics affecting cardiac mechanics. Additionally, the use of myocardial deformation imaging and 3D echocardiography has gained popularity for enhanced assessment of cardiac function and anatomy. This paper discusses the role of echocardiography in evaluating cardiac mechanics in most significant CHDs, particularly its ability to accommodate and interpret the inherent anatomical substrate in these conditions.

Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients

BACKGROUND

Significant interobserver and interstudy variability occurs for left ventricular (LV) functional indices despite standardization of measurement techniques. Artificial intelligence models trained on adult echocardiograms are not likely to be applicable to a pediatric population. We present EchoNet-Peds, a video-based deep learning algorithm, which matches human expert performance of LV segmentation and ejection fraction (EF).

METHODS

A large pediatric data set of 4,467 echocardiograms was used to develop EchoNet-Peds. EchoNet-Peds was trained on 80% of the data for segmentation of the left ventricle and estimation of LVEF. The remaining 20% was used to fine-tune and validate the algorithm.

RESULTS

In both apical 4-chamber and parasternal short-axis views, EchoNet-Peds segments the left ventricle with a Dice similarity coefficient of 0.89. EchoNet-Peds estimates EF with a mean absolute error of 3.66% and can routinely identify pediatric patients with systolic dysfunction (area under the curve of 0.95). EchoNet-Peds was trained on pediatric echocardiograms and performed significantly better to estimate EF (P < .001) than an adult model applied to the same data.

CONCLUSIONS

Accurate, rapid automation of EF assessment and recognition of systolic dysfunction in a pediatric population are feasible using EchoNet-Peds with the potential for far-reaching clinical impact. In addition, the first large pediatric data set of annotated echocardiograms is now publicly available for efforts to develop pediatric-specific artificial intelligence algorithms.