Automated deep learning approach for identifying etiologies of hypertrophic phenocopies by echocardiography
Authors: Su P H , Kuo L , Yu W C , Lu C F 
Published: October 2024

Background

Left ventricular hypertrophy (LVH) is an indicator of poor prognosis across various cardiovascular diseases and is developed by various etiologies. Transthoracic echocardiography (TTE) is the most available cardiac imaging tool to detect LVH. However, it is still challenging to differentiate three non-ischemic cardiomyopathies which frequently presenting LVH: Fabry cardiomyopathy (FC), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA) from morphology or cardiac function evaluated by TTE.

Purpose

We aimed to develop stepwise deep learning models to differentiate FC, HCM, and CA using radiomics extracted from TTE.

Methods

We conducted a retrospective cohort study between 2011 and 2022 in Taiwan. Patients with left ventricular (LV) mass index≥95g/m² in women or ≥115g/m² in men, or the maximal LV wall thickness≥13mm on echocardiography were enrolled. The TTE automatic processing models included four steps: image quality assessment, view classification, auto-segmentation of interventricular septum (IVS), and dynamic radiomics analysis.

One echocardiographic study was scanned per patient, and each study contains multiple cardiac cycles. One sample was extracted from each cycle and all samples were evaluated by image quality assessment and view classification model that based on VGG16. Samples with apical four (A4C) and apical five chamber (A5C) views were retained for following steps. Manual contouring of the septal region served as the ground truth for auto-segmentation based on U-Net. The last step was the extraction of dynamic radiomic features from IVS and the dynamic radiomic features were analyzed using an LSTM model to differentiate three hypertrophic cardiomyopathies.

Results

There were 103 patients with FC, 96 patients with HCM, and 49 patients with CA in this study. Patients with CA were older and had lower LV ejection fraction (EF) than those with FC or HCM (age: 65.8±9.7 vs. 59.3±11.1 vs. 58.6±14.2 years, P=0.002; LVEF: 56.6±10.8 vs. 61.9±8.8 vs. 64.5±8.3%, P&lt;0.001). In addition, patient with FC had larger LV mass index than those with HCM or CA. (Fig. 2A) The details of the performances of four stepwise models were provided in Figure 2 (B-F). The image quality assessment and view classification models achieved accuracies of 91.7% and 95.2%, respectively. The auto-segmentation had the median Dice score as 0.88 for contouring IVS. The results indicated that deep learning algorithms had great ability to recognize different views with proper image quality, and could contour the IVS adequately. The accuracy of dynamic radiomics-based LSTM model to differentiate three hypertrophic phenocopies was 74.5%.

Conclusion

We developed an automatic process with four stepwise models for analyzing the radiomics of IVS on TTE to differentiate FC, HCM and CA with the modest performance by LSTM model. The developed automatic models may provide potential implications to identify various etiologies LVH in the future.Figure 1-Workflow of this studyFigure 2-Performances of stepwise models