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Automatic parameter obtained by artificial intelligence in CMR for the assessment of left ventricular function in patients with dilated and hypertrophic cardiomyopathy
Session:
Sessão de Posters 55 - RM Cardíaca
Speaker:
Miguel Carias
Congress:
CPC 2024
Topic:
B. Imaging
Theme:
03. Imaging
Subtheme:
03.6 Cross-Modality and Multi-Modality Imaging Topics
Session Type:
Cartazes
FP Number:
---
Authors:
Miguel Carias De Sousa; Marta Paralta; António Almeida; Rafael Viana; Bruno Piçarra; Ângela Bento; Manuel Trinca
Abstract
<p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong><span style="font-size:10.0pt">Introduction:</span></strong><span style="font-size:10.0pt"> Artificial intelligence (AI) plays a crucial role in the assessment of left ventricular (LV) function in cardiac magnetic resonance (CMR) due to its ability to streamline and enhance the analysis of complex imaging data. The generation of automatic parameters for LV function can revolutionize the way cardiac imaging data is analyzed, offering greater efficiency, accuracy, and potential for early detection and personalized treatment strategies.</span></span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong><span style="font-size:10.0pt">Purpose:</span></strong><span style="font-size:10.0pt"> This study aims to determine if there is a relationship between the measurement of longitudinal LV shortening and other functional parameters in CMR within a clinical setting.</span></span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong><span style="font-size:10.0pt">Methods:</span></strong><span style="font-size:10.0pt"> We retrospectively analyzed a population of patients submitted to CMR and divided them into three groups: those without structural disease, those with dilated cardiomyopathy (DCM) and those with hypertrophic cardiomyopathy (HCM). We documented demographic factors, LV ejection fraction (LVEF) and the longitudinal LV shortening obtained through AI in CMR for all groups. We then performed univariate analysis by Pearson correlation to establish the relationship between variables.</span></span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong><span style="font-size:10.0pt">Results:</span></strong><span style="font-size:10.0pt"> Out of 103 patients, 22,3% (n=23) had no structural disease, considered the control group, 37,9% (n=39) had HCM and 39.8% (n=41) had DCM. 59,2% were male, with mean age of 55</span><span style="font-size:10.0pt">±</span><span style="font-size:10.0pt">16 years, with no differences between groups. While both the control and HCM had preserved LVEF (58,65% vs 61,95%), there were significant differences between the longitudinal LV shortening (-16,94% vs -11,30%, p<0,001). Similar results were verified between the control and DCM group – they had significantly lower LVEF (58,65% vs 30,51%, p<0,001) and longitudinal LV shortening (-16,94% vs -7,19%, p<0,001). Overall, there is a strong positive correlation (r=0,672, p<0,001) between longitudinal LV shortening and the LVEF.</span></span></span></p> <p><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><strong><span style="font-size:10.0pt">Conclusions:</span></strong><span style="font-size:10.0pt"> There is a strong positive correlation between longitudinal LV shortening and the LVEF. In HCM, even with preserved LVEF, there is a significantly decreased value of longitudinal LV shortening when compared to normal controls, which suggests this AI generated data could be an early predictor of subclinical disease progression and functional decay even before there is a significant decrease in LVEF.</span></span></span></p>
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