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

arXiv.org, 2024-04

2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2404.16522

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  • Title:
    A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography
  • Author: Peng, Bo ; Li, Xiaofeng ; Li, Xinyu ; Wang, Zhenghan ; Deng, Hui ; Luo, Xiaoxian ; Yin, Lixue ; Zhang, Hongmei
  • Subjects: Amyloidosis ; Cardiomyopathy ; Classification ; Computer Science - Learning ; Deep learning ; Echocardiography
  • Is Part Of: arXiv.org, 2024-04
  • Description: 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.
  • Publisher: Ithaca: Cornell University Library, arXiv.org
  • Language: English
  • Identifier: EISSN: 2331-8422
    DOI: 10.48550/arxiv.2404.16522
  • Source: World Web Journals
    arXiv.org
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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