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Artificial intelligence in cardiac magnetic resonance – the next step in predict atrial fibrillation?
Session:
SESSÃO DE POSTERS 47 - AVALIAÇÃO CARDÍACA POR TC E/OU RM
Speaker:
Marta Paralta De Figueiredo
Congress:
CPC 2025
Topic:
B. Imaging
Theme:
03. Imaging
Subtheme:
03.3 Cardiac Magnetic Resonance
Session Type:
Cartazes
FP Number:
---
Authors:
Marta Paralta De Figueiredo; Rafael Viana; Antonio Almeida; Miguel Carias; Rita Louro; Orlando Luquengo; Diogo Bras; Bruno Piçarra; Manuel Trinca
Abstract
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Introduction: Atrial fibrillation (AF) is an increasingly frequent comorbidity that increases the risk of stroke and mortality. Artificial intelligence (AI) plays a vital role in cardiac magnetic resonance (CMR) due to its ability to streamline and enhance the analysis of complex imaging data. These automatically generated parameters can potentially unlock earlier diagnosis and personalized treatment strategies.</span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Purpose: Our study aimed to investigate if there were AI-derived CMR parameters associated with AF.</span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Methods: We retrospectively analyzed a population of patients submitted to CMR and divided them in two groups – those with and without AF. We documented demographic factors, left atrial (LAEF) and ventricular ejection fraction (LVEF), ventricular and atrial volumes and the longitudinal LA and LV shortening obtained through AI in CMR. We then performed univariate analysis to establish the relationship between variables and multivariate analysis to identify independent predictors.</span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Results: Out of 103 patients, 22,3% (n=23) had no structural disease, 37,9% (n=39) had HCM and 39.8% (n=41) had DCM. 59,2% were male, with mean age of 55±16 years, with no differences between groups. When comparing groups regarding history of AF, these patients had similar left ventricular ejection fraction (LVEF), with a median of 47±17%, ventricular systolic and diastolic volumes and longitudinal ventricular shortening, as well as left and right atrial longitudinal shortening. However, patients with AF had significantly lower biplane LAEF (37,7% vs 51,6%, p=0,003) and higher indexed diastolic biplane LA volume (64,8mL vs 40,4mL, p= 0,007). A ROC curve was evaluated revealing a strong sensitivity for indexed diastolic biplane LA volume as an early diagnostic marker of AF (AUC=0,714), with a cutoff value of 29,4mL presenting 93% sensitivity and 21,3% specificity, while volumes above 60,4mL have 62,5% sensitivity and 84% specificity for diagnosing AF.</span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif">Conclusion: In patients submitted to CMR there is a positive association between higher indexed diastolic biplane LA volume and history of AF, regardless of having structural disease. This AI generated parameter has a strong discriminatory ability for diagnosing AF, possibly contributing to earlier diagnosis and stroke prevention.</span></span></p>
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