LATEST TECHNOLOGIES IN THE DIAGNOSIS OF LEFT ATRIAL CARDIOMYOPATHY: FROM CONCEPT TO CLINICAL IMPLEMENTATION OF NON-INVASIVE IMAGING METHODS
DOI:
https://doi.org/10.30890/2567-5273.2025-40-02-042Keywords:
left atrial cardiomyopathy, artificial intelligence, 4D echocardiography, parametric MRI mapping, aggressive course of atrial fibrillation.Abstract
The paper analyzes the capabilities of the latest non-invasive imaging technologies for the diagnosis of left atrial cardiomyopathy in patients with atrial fibrillation and substantiates the feasibility of introducing these methods into clinical practice.References
Kottkamp H. Human atrial fibrillation substrate: towards a specific fibrotic atrial cardiomyopathy. Eur Heart J. 2013;34(35):2731-2738.
Hindricks G, Potpara T, Dagres N, et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation. Eur Heart J. 2021;42(5):373-498.
Habibi M, Samiei S, Ambale Venkatesh B, et al. Cardiac magnetic resonance-measured left atrial volume and function and incident atrial fibrillation: results from MESA (Multi-Ethnic Study of Atherosclerosis). Circ Cardiovasc Imaging. 2016;9(8):e004299.
Thomas L, Marwick TH, Popescu BA, Donal E, Badano LP. Left atrial structure and function, and left ventricular diastolic dysfunction: JACC scientific statement. J Am Coll Cardiol. 2019;73(15):1961-1977.
Marrouche NF, Wilber D, Hindricks G, et al. Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study. JAMA. 2014;311(5):498-506.
Bisbal F, Guiu E, Cabanas-Grandío P, et al. CMR-guided approach to localize and ablate gaps in repeat AF ablation procedure. JACC Cardiovasc Imaging. 2014;7(7):653-663.
Firouznia M, Feeny AK, LaBarbera MA, et al. Machine learning-derived fractal features of shape and texture of the left atrium and pulmonary veins from cardiac computed tomography scans are associated with risk of recurrence of atrial fibrillation post-ablation. Circ Arrhythm Electrophysiol. 2021;14(6):e009265.
Jo YY, Cho Y, Lee SY, et al. Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram. Int J Cardiol. 2021;328:104-110.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Authors

This work is licensed under a Creative Commons Attribution 4.0 International License.



