AI-based calcium quantification using 3D TEE is feasible and correlates with CT-derived scores, and this radiation-free approach may provide a promising alternative for assessing aortic valve calcification.
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.
STUDY TYPE
Retrospective monocentric study.
PURPOSE
To estimate the probability of severe aortic stenosis(SAS) by calculating the aortic valve calcium score (AVCS) using virtual noncontrast (VNC) reconstructions derived from non-ECG-gated dual-energy computed tomography (DECT) scans.
MATERIALS AND METHODS
This study retrospectively included 59 patients who underwent DECT scans for noncardiac indications and presented with visible aortic valve calcifications. AVCS was calculated on VNC images using semiautomatic software. All patients had an echocardiogram performed within 12 months before or after the DECT scan. We then analyzed the correlation between AVCS values and the degree of aortic stenosis.
RESULTS
Both male and female patients with SAS had a significantly higher mean AVCS (919.2 AU, SD=1262.4) compared with those with nonsevere aortic stenosis (NSAS) (127.9 AU, SD=278.6). AVCS demonstrated good diagnostic performance in distinguishing between severe and nonsevere AS in both female and male (AUC=0.91 and 0.95, respectively). Estimated thresholds values showed high sensitivity in both genders, with good specificity in men, and moderate in women. When analyzed irrespective of gender, AVCS demonstrated good ability to distinguish severe AS from mild and no stenosis (AUC=0.926), and moderate ability to distinguish moderate AS from mild and no stenosis (AUC=0.781). However, it lacked statistical significance in discriminating severe and moderate AS (AUC=0.714, P=0.1325).
CONCLUSIONS
Aortic Valve Calcium Score obtained on VNC images from DECT examinations is a promising method for noninvasively differentiation between severe and nonsevere aortic stenosis, which could be potentially useful in patients who have undergone examinations for noncardiac indications.
Jakub Byczkowski, Gregorio Chierchia, G. Muscogiuri et al.· Journal of thoracic imaging· 0 citations
To determine whether higher Hounsfield unit (HU) thresholds for computed tomography-derived aortic valve calcium (CT-AVC) scoring provide additional hemodynamic or discriminatory value beyond conventional 130-HU Agatston scoring in classical high-flow/high-gradient severe aortic stenosis (AS). This single-center retrospective cohort included 63 consecutive pre-TAVI patients with trileaflet, classical high-flow/high-gradient severe AS. CT-AVC was quantified on non-contrast ECG-gated CT at 130, 200, 500, 800, and 1000 HU within a manually defined leaflet/annulus region of interest, excluding left ventricular outflow tract and mitral annular calcification. Associations with peak gradient, mean gradient, and aortic valve area were assessed using correlation and multivariable linear regression.Exploratory ROC analysis assessed discrimination of very severe hemodynamic burden, defined as a mean gradient ≥ 60 mmHg, within the established severe-AS cohort, with ROC areas compared using DeLong testing. CT-AVC at all HU thresholds was independently associated with higher peak gradient (β, 0.010 at 130 HU to 0.144 at 1000 HU; all P ≤ 0.005) and higher mean gradient (β, 0.0059 at 130 HU to 0.073 at 1000 HU; all P ≤ 0.024). Associations with aortic valve area were not statistically significant. Within-cohort discrimination of very severe hemodynamic burden was modest and comparable across thresholds (AUC 0.65-0.67), with no statistically significant difference by DeLong testing (P = 0.68). In classical high-flow/high-gradient severe AS, CT-AVC demonstrated consistent associations with transvalvular gradients across HU thresholds. Higher HU thresholds did not outperform conventional 130-HU scoring and showed only modest, comparable performance for within-severity hemodynamic stratification. These thresholds should be interpreted as complementary densitometric analyses rather than alternative diagnostic cut-offs.
Michael Welt, M. Alnees, Yazan Hamdan et al.· The International Journal of...· 0 citations
CT-derived AVA demonstrates strong agreement with TTE and provides complementary information for severity assessment and risk stratification in patients with aortic stenosis, particularly in cases with discordant or borderline findings.
Shehroz Sultan, Neeraj Joshi, A. H. Awan et al.· 0 citations
Aortic stenosis (AS) is the most common degenerative valvular disease in elderly patients and is linked to high morbidity and mortality. Accurate diagnosis and risk stratification are critical for effective management. Transthoracic echocardiography is the standard diagnostic tool, but its reliance on flow-dependent parameters can lead to inconsistent grading, especially in low-flow, low-gradient, or normal-flow, low-gradient AS. Advanced echocardiographic methods, such as 3D imaging, stress echocardiography, and Doppler indices, such as the mean gradient-to-effective orifice area ratio, improve the evaluation of AS severity and assist in clinical decision-making. Computed tomography provides a flow-independent evaluation of AS. It uses noncontrast calcium scoring with sex-specific thresholds, along with contrast-enhanced angiography, for detailed anatomical assessment. These modalities are essential for procedural planning, particularly for transcatheter aortic valve replacement. Cardiac magnetic resonance (CMR) provides additional prognostic information. It quantifies myocardial remodeling and fibrosis, which are associated with outcomes and recovery potential. Emerging technologies are expanding diagnostic capabilities in AS. Examples include 18F-sodium fluoride positron emission tomography for detecting microcalcification, artificial intelligence-based ECG and echocardiography for early diagnosis, and 4D flow CMR. Integration of echocardiography, computed tomography, CMR, and emerging positron emission tomography and artificial intelligence-based approaches can help address diagnostic uncertainty. This integration helps refine AS subtype classification and inform individualized intervention strategies.
H. Itani, M. Moumneh, A. Zayed et al.· Cardiology in Review· 0 citations
MitralVision reliably distinguishes clinically significant MR using single-view B-mode echocardiography without Doppler input for model inference and may support more standardized MR screening.
R. Sandler, J. Sokol, S.G. Pawar et al.· Journal of the American Soci...· 0 citations