Deep learning for evaluating left atrium stress echocardiography: a proof of principle study
Authors: Karuzas A , Sablauskas K , Wierzbowska-Drabik K , Dirsiene R , Fukson M , Kiziela A , Matuliauskas D , Balciunas J , Verikas D , Strioga M , Kasprzak J , Lesauskaite V , Ciampi Q , Picano E 
Published: October 2022

Abstract

Introduction
Left atrial (LA) size is a dynamic variable that changes during stress echocardiography (SE) and provides valuable information within and beyond coronary artery disease. However, its measurement remains subjective, time-consuming and based on manual tracings.
Recent advances in deep learning might save analysis time and deflate variability by removing the subjectivity of LA assessment.

Purpose
In this proof of principle study we aim to validate the potential of an automated machine learning system for the evaluation of LA in SE.

Methods
From the image data bank of Stress Echo 2030 study, we selected 20 consecutive patients who underwent SE (using a variety of stress methods) in 2 recruiting centers. Imaging data was acquired in DICOM format and anonymized. The studies were reviewed by an expert cardiologist trained in SE evaluation who selected apical four chamber (A4Ch) view images during stress and rest phases and marked end-systolic (ES) and end-diastolic frames to be used in further evaluation. Endocardial borders for LA were traced in ES. The tracings were repeated by three different evaluators (expert cardiologist [C1], cardiologist from external center [C2] and a machine learning [ML] model – a convolutional neural network trained on an unrelated set of images). LA area (LAA), volume (LAV) using area-length formula and proportional LAV changes between stress and rest (ΔLAV = [rest − stress] / rest) were calculated. Each evaluator was blinded from each other's measurements.

Results
In total, 40 A4Ch images were acquired (20 at rest and 20 at stress) of which all were of sufficient quality for performing LA measurements using an automated system. Pearson correlation coefficients (R) for LAA were 0.95 (C1-ML), 0.96 (C2-ML) at rest and 0.88 (C1-ML), 0.79 (C2-ML) at stress. The C1-C2 pair had R of 0.98 and 0.86 for LAA at rest and stress.
LAV also showed good correlation between different raters with R values of 0.90 (C1-ML), 0.94 (C2-ML), 0.94 (C1-C2) at rest and 0.86 (C1-ML), 0.87 (C2-ML), 0.84 (C1-C2) at stress. Root mean squared errors (RMSEs) for LAV were 13.48 ml (C1-ML), 7.44 ml (C2-ML), 10.26 ml (C1-C2) at rest and 14.03 ml (C1-ML), 8.53 ml (C2-ML), 13.69 ml (C1-C2) at stress.
There were high level correlations between all raters for ΔLAV with R values of 0.93, 0.94 and 0.95 for C1-ML, C2-ML and C1-C2 pairs respectively. Mean (95% CI) ΔLAV values were −20.1 (−51.59 to +11.39), −14.95 (−43.77 to +13.87) and −14.37 (−34.46 to +5.72) for ML, C1 and C2. Comparison with one-way ANOVA did not show significant differences in mean ΔLAV values between operators (p=0.94).

Conclusions
Automated ML based system produces LA measurements that are comparable to human operators and can reduce the need for manual tracing. There was a tendency for all operators to have lower levels of agreement in stress images compared to rest, further showing the need for additional standardization of SE evaluation for machine and human operators.

Funding Acknowledgement
Type of funding sources: None.