A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography
CoRR(2024)
摘要
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.
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