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.
Curvature analysis provides a novel and sensitive measure of RV remodeling, while AS and LS offer detailed insight into regional RV dysfunction.
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.
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.
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.