TL;DR
AI-based 3D TTE semi-automated measurement has the potential to enhance the efficiency and reproducibility of RV function assessment, reducing examiner workload and improving clinical workflow.
Artificial intelligence-assisted echocardiography showed comparable agreement for aortic root and LVOT measurements, but physician oversight remains necessary.
Tetralogy of Fallot (ToF) accounts for approximately 7-10% of all congenital heart disease and is frequently complicated by right ventricular (RV) dysfunction. Three-dimensional echocardiography (3DE) offers more accurate assessment of RV volume and function, facilitating advanced evaluation of RV geometry. Building on this, a custom software tool was developed to quantify RV shape and function. This study aimed to assess the differences of 3DE derived RV shape and function parameters in ToF and healthy controls. 3DE RV datasets were acquired in 50 ToF and 50 healthy controls. RVs were segmented using commercially available software (TomTec) and further processed with custom software to quantify mean curvature (MC), area strain (AS), and longitudinal strain (LS). Measurements were performed globally and regionally across predefined RV segments. Curvature analysis identified the posterior boundary (253% [227-265] vs. 283% [260-301] vs. 294% [264-329]; for volume overload, absent overload and healthy controls, respectively; p < 0.001) and apex (464% [419-529] vs. 501% [467-541] vs. 554 [486-619]; for volume overload, absent overload and healthy controls, respectively; p < 0.001) as key regions driving RV remodeling in ToF. Septal flattening was observed in ToF with decreased concavity of the SB (p = 0.007). Global RV AS was significantly reduced in ToF (p < 0.05), with the largest impairment observed at the apex (-16% [-21;-12] vs. -14% [-17;-6] vs. -38% [-42;-28]; for volume overload, absent overload and healthy controls, respectively; p < 0.001). This in-house developed software enables comprehensive RV morphology and deformation analysis from standard 3DE imaging. Curvature analysis provides a novel and sensitive measure of RV remodeling, while AS and LS offer detailed insight into regional RV dysfunction. Improved characterization of RV remodeling may support clinical decision-making.
BACKGROUND High-quality echocardiography is essential for accurate and reproducible assessment of cardiac functional indices, which are highly dependent on adequate image quality and proper probe alignment. An artificial intelligence (AI)-based approach may enable automated image quality assessment. AIMS Our aim was to develop and internally validate an AI-based algorithm to predict image quality from selected echocardiographic frames in candidates for implantable cardioverter-defibrillator and cardiac resynchronization therapy with a defibrillator implantation. METHODS In this retrospective cross-sectional study, 248 patients (297 echocardiographic examinations) were included. Demographic, electrocardiographic, echocardiographic, and clinical data were collected. Apical 2-, 3-, and 4-chamber views were extracted for image quality analysis, yielding a total of 909 echocardiograms. Image quality was assessed using end-diastolic frames. An internally validated scoring framework was applied, demonstrating high interclass correlation. RESULTS Regression models provided more clinically relevant information than classification models. Visual transformer models achieved Pearson correlation coefficients similar to those of convolutional neural networks (up to 0.812 and 0.772, respectively; P = 0.31). Architectures trained on end-diastolic frames achieved comparable Pearson correlation coefficients to those trained on combined end-diastolic and end-systolic frames. Compared with human experts, the models showed significantly higher absolute percentage errors, with values of 11%-12% (median) vs. 7.9% (mean) for the total image quality score and 13%-14% (median) vs. 8.8% (mean) for the border quality score. CONCLUSIONS Regression models demonstrated the highest performance. An internally validated AI model can predict an echocardiographic image quality score in a small cohort of cardiac resynchronization therapy with a defibrillator/implantable cardioverter-defibrillator candidates. However, external and prospective validation will be required to establish its generalizability, reliability, and utility before clinical application.
Two-dimensional ultrasound is optimal for screening; RT-3DE shows highest consistency with cardiovascular magnetic resonance for precise diagnosis, and 3DE-LACI has demonstrated independent prognostic value in cardiac amyloidosis.
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.