Exportar Publicação

Fazendas, P., Bairros, R., Elvas, L. B., Brochado, L., Ferreira, J. C., Gomes, R....Almeida, A. G. (2026). Artificial intelligence–based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: A pilot study. Journal of Cardiovascular Imaging. 34 (1)

P. Fazendas et al.,  "Artificial intelligence–based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: A pilot study", in Journal of Cardiovascular Imaging, vol. 34, no. 1, 2026

@article{fazendas2026_1784195857527,
	author = "Fazendas, P. and Bairros, R. and Elvas, L. B. and Brochado, L. and Ferreira, J. C. and Gomes, R. and Pereira, A. R. and Martins, C. and Pereira, J. and Lourenço, C. and Brandão, T. and Pereira, H. and Almeida, A. G.",
	title = "Artificial intelligence–based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: A pilot study",
	journal = "Journal of Cardiovascular Imaging",
	year = "2026",
	volume = "34",
	number = "1",
	doi = "10.1186/s44348-026-00080-x",
	url = "https://link.springer.com/journal/44348"
}

TY  - JOUR
TI  - Artificial intelligence–based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: A pilot study
T2  - Journal of Cardiovascular Imaging
VL  - 34
IS  - 1
AU  - Fazendas, P.
AU  - Bairros, R.
AU  - Elvas, L. B.
AU  - Brochado, L.
AU  - Ferreira, J. C.
AU  - Gomes, R.
AU  - Pereira, A. R.
AU  - Martins, C.
AU  - Pereira, J.
AU  - Lourenço, C.
AU  - Brandão, T.
AU  - Pereira, H.
AU  - Almeida, A. G.
PY  - 2026
SN  - 2586-7210
DO  - 10.1186/s44348-026-00080-x
UR  - https://link.springer.com/journal/44348
AB  - Background: 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.
Methods: 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.
Results: 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%).
Conclusions: 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.
ER  -