Automatic Prediction of Paediatric Cardiac Output From Echocardiograms Using Deep Learning Models

Cardiac output (CO) perturbations are common and cause significant morbidity and mortality. Accurate CO assessment is crucial for guiding treatment in anaesthesia and critical care, but measurement is difficult, even for experts. Artificial intelligence methods show promise as alternatives for accurate, rapid CO assessment.

We reviewed paediatric echocardiograms with normal CO and a dilated cardiomyopathy patient group with reduced CO. Experts measured the left ventricular outflow tract diameter, velocity time integral, CO, and cardiac index (CI). EchoNet-Dynamic is a deep learning model for estimation of ejection fraction in adults. We modified this model to predict the left ventricular outflow tract diameter and retrained it on paediatric data. We developed a novel deep learning approach for velocity time integral estimation. The combined models enable automatic prediction of CO. We evaluated the models against expert measurements. Primary outcomes were root-mean-squared error, mean absolute error, mean average percentage error, and coefficient of determination (R²).

In a test set unused during training, CI was estimated with the root-mean-squared error of 0.389 L/min/m², mean absolute error of 0.321 L/min/m², mean average percentage error of 10.8%, and R² of 0.755. The Bland-Altman analysis showed that the models estimated CI with a bias of +0.14 L/min/m² and 95% limits of agreement -0.58 to 0.86 L/min/m².

Our model estimated CO with strong correlation to ground truth and a bias of 0.17 L/min, better than many CO measurements in paediatrics. Model pretraining enabled accurate estimation despite a small dataset. Potential uses include supporting clinicians in real-time bedside calculation of CO, identification of low-CO states, and treatment responses.