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Calcium Identification and Scoring Based on 3D Transesophageal Echocardiography - An Exploratory Study on Aortic Valve Stenosis
Session:
SESSÃO DE COMUNICAÇÕES ORAIS 19 - DOENÇA VALVULAR
Speaker:
Paula Fazendas
Congress:
CPC 2025
Topic:
N. E-Cardiology / Digital Health, Public Health, Health Economics, Research Methodology
Theme:
33. e-Cardiology / Digital Health
Subtheme:
33.1 Image Processing and Imaging Standards
Session Type:
Comunicações Orais
FP Number:
---
Authors:
Paula Fazendas; Rita Bairros; Luís Brito Elvas; Liliana Brochado; João Carlos Ferreira; Ana Rita Pereira; Cristina Martins; José Pereira; Cândida Lourenço; Tomás Brandão; Hélder Pereira; Ana G. Almeida
Abstract
<p>Introduction: Calcium score of the aortic valve has emerged as a tool for assessing aortic valve severity. Computed tomography (CT) is needed for calcium quantification according to the Agatston score. The use of CT scans is limited due to ionizing radiation and availability.</p> <p>Calcium identification based on echo pixels using artificial intelligence (AI) systems have shown promising results in transthoracic echocardiography (TTE). Nevertheless, this technique is highly dependent on the patient’s acoustic window. We propose that transesophageal echocardiography (TEE) obviates the poor acoustic window and could be used for sequential follow-up of patients since no ionizing radiation is used. We also propose that 3D TEE could be more adequate to estimate total valve calcium burden because it allows for identification of calcium pixels in a greater portion of the aortic valve, when compared to 2D techniques.</p> <p>Objective: Quantification by AI of the calcium burden of the aortic valve by 3D TEE (Standard method: Computer tomography).</p> <p>Materials and methods: Prospective study. Population: 14 individuals, 6 males, 13 patients with moderate or severe aortic stenosis by TTE, median age 77 years (IQR 15).</p> <p>Imagiologic studies: TEE exam: 3D volume sets acquired in 3D zoom at the level of the aortic valve, stored in DICOM for post-processing. In the MPR quantification software contiguous 1.5 mm slices were obtained of the aortic valve in diastole, in short axis view. A Computer Vision (CV) model was applied to echocardiographic images, via adaptive image segmentation and Deep Learning to identify speckles and artifacts generated by the presence of calcium and hence quantify the amount of calcification of the valve. To train the model, images from a healthy control were used. The concordance of the Ca speckles of the 3D TEE images and the Agatston score was compared.</p> <p>Results: CT Ca score: mean 2391+-1176; AI_Ca_score: 491652+-240952, The delay between TEE and CT scans was 55 days (IQR 75), The AI Ca score showed a significant positive correlation with the CT Agatston Ca_score: R= 0,72 (CI 95 % 0,30-0,90) (fig.1). The ROC curve analysis to detect very likely or likely severe calcification showed an excellent result with an AUC of 1 (figure2A) for a cutoff of 300068 in the AI Ca Score, and a good result to detect very likely severe calcification with an AUC of 0,73 (figure2B) for a cutoff of 471061,5 in the AI Ca Score, with a Sensibility of 80% and Specificity of 67 %.</p> <p>Conclusions: in this pilot study we conclude that identification of calcification of the aortic valve by AI from TEE 3D images is feasible and correlates positively with CT scans with a good performance do detect severe calcification. This model should be applied to further ranges of aortic valve calcification to better discriminate severity of the disease.</p> <p> </p>
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