In Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
BACKGROUND : Significant interobserver and interstudy variability occurs for left ventricular 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 left ventricular (LV) segmentation and ejection fraction (EF).
METHODS : A large pediatric dataset of 4,467 echocardiograms were used to develop EchoNet-Peds. EchoNet-Peds was trained on 80% of the data for segmentation of the left ventricle and estimation of left ventricular EF. The remaining 20% was used to fine tune and validate the algorithm.
RESULTS : In both apical 4-chamber (A4C) and parasternal short-axis views (PSAX), 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 < 0.001) than an adult model applied to the same data.
CONCLUSION : 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 dataset of annotated echocardiograms is now publicly available for efforts to develop pediatric-specific artificial intelligence algorithms.
Reddy Charitha D, Lopez Leo, Ouyang David, Zou James Y, He Bryan
2023-Feb-06
Artificial Intelligence, Left Ventricle, Machine Learning, Segmentation, Ventricular Function
