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Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques

Malitha Gunawardhana, Anuradha Kulathilaka, Jichao Zhao

TL;DR

This review addresses atrial fibrillation and the pivotal role of left atrial fibrosis as detected by LGE-MRI, emphasizing deep learning approaches for scar segmentation. It surveys the mechanism, remodeling, and diagnostic imaging foundations of AFib, then delves into CNN-based methods, robustness strategies, and evaluation metrics used in cardiac image analysis. A detailed examination of AI-driven scar segmentation from LGE-MRI is provided, including major challenges, competitions like LAScarQS, and independent research efforts. The paper argues for a shift toward unsupervised and domain-specific DL models to overcome data scarcity and improve clinical applicability in AFib management.

Abstract

Atrial fibrillation (AFib) is the prominent cardiac arrhythmia in the world. It affects mostly the elderly population, with potential consequences such as stroke and heart failure in the absence of necessary treatments as soon as possible. The importance of atrial scarring in the development and progression of AFib has gained recognition, positioning late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) as a crucial technique for the non-invasive evaluation of atrial scar tissue. This review delves into the recent progress in segmenting atrial scars using LGE-MRIs, emphasizing the importance of precise scar measurement in the treatment and management of AFib. Initially, it provides a detailed examination of AFib. Subsequently, it explores the application of deep learning in this domain. The review culminates in a discussion of the latest research advancements in atrial scar segmentation using deep learning methods. By offering a thorough analysis of current technologies and their impact on AFib management strategies, this review highlights the integral role of deep learning in enhancing atrial scar segmentation and its implications for future therapeutic approaches.

Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques

TL;DR

This review addresses atrial fibrillation and the pivotal role of left atrial fibrosis as detected by LGE-MRI, emphasizing deep learning approaches for scar segmentation. It surveys the mechanism, remodeling, and diagnostic imaging foundations of AFib, then delves into CNN-based methods, robustness strategies, and evaluation metrics used in cardiac image analysis. A detailed examination of AI-driven scar segmentation from LGE-MRI is provided, including major challenges, competitions like LAScarQS, and independent research efforts. The paper argues for a shift toward unsupervised and domain-specific DL models to overcome data scarcity and improve clinical applicability in AFib management.

Abstract

Atrial fibrillation (AFib) is the prominent cardiac arrhythmia in the world. It affects mostly the elderly population, with potential consequences such as stroke and heart failure in the absence of necessary treatments as soon as possible. The importance of atrial scarring in the development and progression of AFib has gained recognition, positioning late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) as a crucial technique for the non-invasive evaluation of atrial scar tissue. This review delves into the recent progress in segmenting atrial scars using LGE-MRIs, emphasizing the importance of precise scar measurement in the treatment and management of AFib. Initially, it provides a detailed examination of AFib. Subsequently, it explores the application of deep learning in this domain. The review culminates in a discussion of the latest research advancements in atrial scar segmentation using deep learning methods. By offering a thorough analysis of current technologies and their impact on AFib management strategies, this review highlights the integral role of deep learning in enhancing atrial scar segmentation and its implications for future therapeutic approaches.
Paper Structure (70 sections, 8 equations, 26 figures, 3 tables)

This paper contains 70 sections, 8 equations, 26 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Atrial fibrillation
  3. Mechanism of Atrial Fibrillation
  4. Atrial Ectopic Foci
  5. Re-entry
  6. Atrial Remodeling
  7. Structural remodeling
  8. At the Cellular Level
  9. At the Tissue Level
  10. At the Organ Level
  11. Electrical remodeling
  12. Contractile remodelling
  13. Diagnostic and Imaging Techniques in Atrial Fibrillation Management
  14. Electrocardiogram (ECG)
  15. Magnetic Resonance Imaging (MRI)
  16. ...and 55 more sections

Figures (26)

  • Figure 1: Global prevalence of atrial fibrillation. The image is adapted from hindricks20212020.
  • Figure 2: Prevalence of atrial fibrillation by Gender and Age in 65,747 subjects. Individuals screened in Belgium during the week of the heart rhythm, 2010-2014. Data includes males (M) and females (F), presented with a 95% confidence interval. Image is taken from mairesse2017screening
  • Figure 3: AFib Categorization. In Category 3, patients can transition from different sub-stages. Figure is modified from joglar20242023
  • Figure 4: Representation of EADs and DADs. Image is taken from iwasaki2011atrial
  • Figure 5: Anatomical re-entry vs functional re-entry. Image is taken from veenhuyzen2004atrial
  • ...and 21 more figures