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

• URL: http://arxiv.org/abs/2407.09561v1
• Date: Mon, 1 Jul 2024 11:33:52 GMT
• Title: Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques
• Authors: Malitha Gunawardhana, Anuradha Kulathilaka, Jichao Zhao,
• Abstract summary: Atrial fibrillation (AFib) is the prominent cardiac arrhythmia in the world. This review delves into the recent progress in segmenting atrial scars using LGE-MRIs. It emphasizes the importance of precise scar measurement in the treatment and management of AFib.
• Score: 0.5461938536945723
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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