AI-based calcium quantification using 3D transesophageal echocardiography significantly correlated with CT Agatston scores (r = 0.65) and identified severe calcification with an AUC of 0.87.
Observational (n=23)
No
Does artificial intelligence-based calcium scoring using 3D transesophageal echocardiography accurately quantify aortic valve calcium compared to computed tomography in patients with moderate or severe aortic stenosis?
AI-based calcium quantification using 3D TEE is feasible and correlates well with CT-derived Agatston scores, offering a potential radiation-free alternative for assessing aortic valve calcification.
Effect estimate: AUC 0.87 (95% CI 0.69-1.00)
Abstract 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.
Fazendas et al. (Thu,) conducted a observational in Aortic stenosis (n=23). AI-based calcium scoring using 3D transesophageal echocardiography vs. CT Agatston score was evaluated on Identification of severe calcification (AUC 0.87, 95% CI 0.69-1.00). AI-based calcium quantification using 3D transesophageal echocardiography significantly correlated with CT Agatston scores (r = 0.65) and identified severe calcification with an AUC of 0.87.