Advances in the Management of Cardiac Amyloidosis and Hypertrophic Cardiomyopathy

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1234

Special Issue Editor


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Guest Editor
Alaska Heart and Vascular Institute, Anchorage, AK 99508, USA
Interests: cardiovascular disease; cardiovascular disease general; noninvasive and advanced cardiac imaging
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Special Issue Information

Dear Colleagues,

This Special Issue, "Advances in the Management of Cardiac Amyloidosis and Hypertrophic Cardiomyopathy", aims to provide a comprehensive overview of the latest developments in the diagnosis, treatment, and management of these two complex cardiac conditions. This Special Issue will feature cutting-edge research articles, reviews, and case studies that explore the pathophysiology, clinical manifestations, and therapeutic strategies for cardiac amyloidosis and hypertrophic cardiomyopathy. Particular emphasis will be placed on the role of non-invasive cardiac imaging techniques in the early detection and monitoring of left ventricular hypertrophy, a hallmark of these disorders. Additionally, this Special Issue will address the impact of these cardiac diseases on heart failure and population health, with a focus on improving diversity and equity in the management of these conditions. This collection of high-quality, peer-reviewed content will serve as a valuable resource for healthcare professionals, researchers, and clinicians seeking to enhance patient outcomes and quality of life.

Dr. Ahmed Abuzaid
Guest Editor

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Keywords

  • cardiac amyloidosis
  • hypertrophic cardiomyopathy
  • left ventricular hypertrophy
  • non-invasive cardiac imaging
  • heart failure
  • clinical manifestations

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Published Papers (1 paper)

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Research

13 pages, 4328 KiB  
Article
Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection
by Jaime Rafael Barón, Gregorio Bernabé, Pilar González-Férez, José Manuel García, Guillem Casas and Josefa González-Carrillo
J. Clin. Med. 2025, 14(1), 271; https://doi.org/10.3390/jcm14010271 - 6 Jan 2025
Viewed by 817
Abstract
Background: Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of [...] Read more.
Background: Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. Methods: We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model. Results: Applied to datasets from three hospitals, these methods demonstrate improved segmentation accuracy, with the blob-selection technique boosting the Dice coefficient for the Trabecular Zone by up to 0.06 in certain populations. Conclusions: Our approach enhances the dataset’s quality, providing a more robust foundation for future LVNC diagnostic models. Full article
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