Curriculum-Based Deep Learning for Automated Segmental Left Ventricular Hypertrophy Classification Using Short-Axis Echocardiography

Abstract

Background: Accurate detection and classification of segmental left ventricular hypertrophy (LVH) are challenging due to rib and reverberation artifacts in two-dimensional short-axis (2D-SAX) echocardiography view. These artifacts lead to ambiguity in the boundaries of the myocardial region. Existing artificial intelligence techniques focus on global LVH detection compared to segmental LVH classification. Methodology: To solve the abovementioned problem, a curriculum-based left ventricular hypertrophy deep learning (LVH-DL) framework is proposed for segmental LVH classification. The proposed framework was tested with 2000 images of segmental LVH, which consists of (a) apical-423, (b) basal-382, (c) mid-ventricular-349, (d) diffuse-453, and (e) normal-393. The curriculum-based deep learning model consists of proposed algorithms such as (i) preprocessing of Mountaineering-Team-Based Optimization Deep Denoised Convolutional Neural Network (MTBO-DnCNN) for removing artifacts and enhancing the boundaries, (ii) segmentation of modified active contour segmentation (MACS) algorithm accurately extracting the myocardium region from the background, and (iii) classification of curriculum-based ConvNeXt-V2 model classifying segmental LVH using inter-channel features such as valvular structures and chamber size from the segmented myocardial region. Results: The proposed LVH-DL framework achieves a classification accuracy of about (a) 96.5% for apical, (b) 95% for basal, (c) 95.8% for mid-ventricular, (d) 97.2% for diffuse, and (e) 95% for normal. Conclusion: Thus, Thus, the proposed LVH-DL framework acts as a decision support tool for early identification of segmental LVH.