Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation

Lyu, Yiheng, Bennamoun, Mohammed, Sharif, Naeha, Lip, Gregory YH ORCID: 0000-0002-7566-1626 and Dwivedi, Girish (2023) Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation. Life, 13 (9). 1870-.

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Abstract

Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.

Item Type:	Article
Uncontrolled Keywords:	artificial intelligence, atrial fibrillation, computed tomography, deep learning, echocardiography, machine learning, magnetic resonance imaging
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	26 Sep 2023 13:18
Last Modified:	11 Oct 2023 14:28
DOI:	10.3390/life13091870
Open Access URL:	https://doi.org/10.3390/life13091870
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3173067