Artificial intelligence to measure left atrial ejection fraction in transthoracic echocardiography videos and its usefulness to assess thromboembolic risk
Authors: Pieszko K , Makowski B , Uzieblo-Zyczkowska B , Bembenek J , Kolasa I , Roszkowski M , Lojewska K , Kwiecinski J , Krzesinski P , Musielak B , Nowak K , Ficner H , Vukadinovic M , Hiczkiewicz J , Ouyang D 
Published: January 2025

Abstract

Background
Left atrial (LA) area measurement in 2D echocardiography is commonly used to estimate LA volume, plays a crucial role in diagnosis and has prognostic implication in a wide range of cardiovascular diseases including thromboembolic risk assessment. However, the manual standard measurement does not include quantification of LA ejection fraction (LAEF), an emerging marker of cardiovascular outcomes.

Purpose
To develop and test an artificial intelligence (AI) model to determine the LA area and LAEF based on apical four-chamber view (A4C) video recordings and evaluate the usefulness of automatically assessed LAEF (AI-LAEF) to predict the left atrial appendage thrombus (LAT).

Methods
We trained a deep learning model for frame-by-frame segmentation of the left atrium using 1,509 A4C view videos with manually created LA tracings. We performed a two-step external testing process: first to evaluate the performance in LA segmentation, and second to evaluate the usefulness of AI-LAEF in predicting LAT.
In the first step, the model was tested on the CAMUS dataset, a publicly available collection of 500 A4C video recordings with LA and left ventricle (LV) tracings available for each video frame. We generated AI segmentations of the LA using our model. The LV AI segmentations were generated for performance comparison using the publicly available state-of-the-art EchoNet LV model. Both LA and LV segmentations were compared with those of expert annotators using the Dice similarity coefficient.
In the second step, we evaluated the model in a cohort of 282 patients from two external sites with same-day transthoracic and transesophageal (TEE) echocardiography. We investigated the predictive performance of AI-LAEF for the presence of LAT using areas under the receiver-operating curves (AUC) and compared it with that of LV ejection fraction measured by clinical echocardiographers.

Results
We excluded 71 patients from the CAMUS dataset due to the A4C view not encompassing the entire LA. In the final testing dataset of 16,429 images from 429 A4C videos, the median Dice coefficient for the LA model was 0.82 (0.72, 0.86) vs. 0.76 ( 0.67, 0.82) for the LV model (p&lt;0.001).
In the second-step testing cohort, the median age was 68 (60, 72) and 23 patients (8.9%) had LAT found in TEE. AI-LAEF demonstrated an AUC of 0.73 (0.65, 0.81) for the prediction of LAT which was not significantly lower than that of the expert-calculated LV ejection fraction - 0.77 (0.65, 0.89), p=0.79.

Conclusions
Presented model provides performance in the measurement of LA area similar to that of state-of-the-art LV segmentation models in an external dataset with LA and LV ground truth segmentations performed by independent researchers. Frame-by-frame segmentation of the LA allowed for the calculation of AI-LAEF, which demonstrated predictive performance in the prediction of LAT comparable to that of LV ejection fraction, making it a promising marker of thromboembolic risk.
Fig1. Dice similarity analysis
Fig2. ROC plot for LAT prediction