A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography

TL;DR

This paper presents a deep learning pipeline that analyzes multi-view 2D echocardiography images to accurately differentiate hypertrophic cardiomyopathy from cardiac amyloidosis, aiding diagnosis with high precision.

Contribution

It introduces a novel multi-view deep learning method that classifies and combines features from five echocardiographic views for disease differentiation.

Findings

Achieved a precision and recall of 0.905

Micro-F1 score of 0.904 demonstrates high accuracy

Effective in differentiating HCM and CA using multi-view analysis

Abstract

Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart conditions that can progress to heart failure if untreated. They exhibit similar echocardiographic characteristics, often leading to diagnostic challenges. This paper introduces a novel multi-view deep learning approach that utilizes 2D echocardiography for differentiating between HCM and CA. The method begins by classifying 2D echocardiography data into five distinct echocardiographic views: apical 4-chamber, parasternal long axis of left ventricle, parasternal short axis at levels of the mitral valve, papillary muscle, and apex. It then extracts features of each view separately and combines five features for disease classification. A total of 212 patients diagnosed with HCM, and 30 patients diagnosed with CA, along with 200 individuals with normal cardiac function(Normal), were enrolled in this study from 2018 to 2022. This approach achieved a precision, recall of 0.905, and micro-F1 score of 0.904, demonstrating its effectiveness in accurately identifying HCM and CA using a multi-view analysis.