Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.
Authors
Affiliations (12)
Affiliations (12)
- Department of Cardiology, Hospital Garcia de Orta, Almada, Portugal. [email protected].
- Cardiovascular Centre at the University of Lisbon (CCUL)-RISE, Faculty of Medicine, University of Lisbon, Lisbon, Portugal. [email protected].
- ISTAR, InstitutoUniversitário de Lisboa (ISCTE-IUL), Lisbon, Portugal.
- ISTAR, InstitutoUniversitário de Lisboa (ISCTE-IUL), Lisbon, Portugal. [email protected].
- Department of Logistics, Molde University College, Molde, Norway. [email protected].
- Inesc Inov Inovação - Instituto de Novas Tecnologias, Lisbon, Portugal. [email protected].
- Breast Cancer Research Program, Champalimaud Foundation, Lisbon, Portugal. [email protected].
- Department of Cardiology, Hospital Garcia de Orta, Almada, Portugal.
- Department of Logistics, Molde University College, Molde, Norway.
- Inesc Inov Inovação - Instituto de Novas Tecnologias, Lisbon, Portugal.
- Department of Radiology, Hospital Garcia de Orta, Almada, Portugal.
- Cardiovascular Centre at the University of Lisbon (CCUL)-RISE, Faculty of Medicine, University of Lisbon, Lisbon, Portugal.
Abstract
Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by radiation exposure and availability. Artificial intelligence (AI)-based calcium detection using transthoracic echocardiography has shown promise but depends on acoustic window quality. Transesophageal echocardiography (TEE), particularly 3D TEE, may overcome these limitations by providing improved visualization without radiation. The objective of this study is to evaluate the feasibility of AI-based quantification of aortic valve calcium using 3D TEE. In this prospective pilot study, 23 patients (median age, 76 years; 56.5% male) with moderate or severe aortic stenosis underwent 3D TEE and CT. Multiplanar reconstruction generated 1.5-mm diastolic short-axis slices. A computer vision-based model identified calcium-related speckles. An automated TEE calcium score was derived from the sum of calcium pixels across 11 frames per patient, which was compared with the CT Agatston score. The TEE calcium score showed a significant positive correlation with CT Agatston scores (r = 0.65, P < 0.001). Receiver operating characteristic analysis yielded an area under the curve of 0.87 (95% confidence interval, 0.69-1.00) for identifying severe calcification (cutoff, 68,813 pixels; sensitivity, 89.5%; specificity, 75.0%). AI-based calcium quantification using 3D TEE is feasible and correlates with CT-derived scores. This radiation-free approach may provide a promising alternative for assessing aortic valve calcification.