Artificial Intelligence Automation of Echocardiographic Measurements

Abstract

Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.

Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.

Methods: We trained models for the automated measurement of echocardiography parameters using data sets between 2011 and 2023 from Cedars-Sinai Medical Center (CSMC). The outputs of segmentation models were compared with sonographer measurements from temporal split data from CSMC and an external data set from Stanford Healthcare (SHC) to access accuracy and precision.

Results: We used 877,983 echocardiographic measurements from 155,215 studies from CSMC to develop EchoNet-Measurements, an open-source deep learning model for echocardiographic annotation. The models demonstrated high accuracy when compared with sonographer measurements from held-out data from CSMC and an independent external validation data set from SHC. Measurements across all 9 B-mode and 9 Doppler measurements had high accuracy (mean coverage probability of 0.796 and 0.839 and mean relative difference of 0.120 and 0.096 on held-out test set from CSMC and external data set from SHC, respectively). When evaluated end-to-end on 2,103 temporally distinct studies at CSMC, EchoNet-Measurements had similar reasonable performance (mean coverage probability 0.803 and mean relative difference of 0.108). Performance was consistent across patient characteristics including age, sex, and atrial fibrillation, obesity status, and machine vendors.

Conclusions: EchoNet-Measurements achieves high accuracy in automated echocardiographic quantification and potential for assisting the clinicians in the echocardiography workflow. This open-source model provides the foundation for future developments in artificial intelligence applied to echocardiography.

Keywords: Doppler wave; automated measurement; convolutional neural network; deep learning; echocardiography.