Real-life clinical validation of artificial intelligence-assisted echocardiography for aortic root and left ventricular outflow tract measurements against human readers and cardiac magnetic resonance
Aug 2026· European Heart Journal - Digital Health· Vol 7· 0 citations· 19 references
Medicine
TL;DR
Artificial intelligence-assisted echocardiography showed comparable agreement for aortic root and LVOT measurements, but physician oversight remains necessary.
Abstract
Abstract Aims Artificial intelligence (AI) algorithms may improve echocardiographic measurement standardization, but direct validation against cardiovascular magnetic resonance (CMR) remains limited. This study aimed to evaluate the agreement between AI-assisted transthoracic echocardiography (TTE), expert manual TTE, and CMR as a gold standard for aortic root and left ventricular outflow tract (LVOT) diameters. Methods and results Out of 183 patients with analysable TTE and CMR recordings performed within 7 days, 169 had complete measurements of LVOT, sinotubular junction (STJ), and sinus of Valsalva (SoV) by AI and were included in the primary analysis. Automated AI measurements were obtained using the US2.ai platform and compared with those of expert echocardiographers and CMR. Agreement was assessed using intraclass correlation coefficients (ICC) and Bland–Altman analysis. Agreement between Expert 1 and CMR, and Expert 2 and CMR, yielded ICCs: 0.63 (95% CI: 0.50–0.73) and 0.51 (95% CI: 0.36–0.65) for LVOT, 0.71 (95% CI: 0.63–0.78) and 0.82 (95% CI: 0.73–0.87) for STJ, and 0.85 (95% CI: 0.79–0.89) to 0.85 (95% CI: 0.78–0.89) for SoV, respectively. Artificial intelligence–derived measurements demonstrated comparable agreement with CMR: ICC 0.70 (95% CI: 0.61–0.77) for LVOT, 0.77 (95% 0.70–0.83) for STJ, and 0.77 (95% 0.70–0.83) for SoV. Clinically significant differences (≥2 mm for LVOT and ≥4 mm for STJ and SoV) between AI and CMR measurements were observed in 36.7% of LVOT, 20% of STJ, and 11.8% of SoV measurements. Conclusion Artificial intelligence–assisted echocardiography showed comparable agreement for aortic root and LVOT measurements, but physician oversight remains necessary.
Routine clinical practice, standard echocardiographic measurements showed overall good reproducibility, with the strongest performance for left ventricular indices.
Raúl Reyes-Toledo, D. G. David-Pardo, G. Lemus-Barrios et al.· Frontiers in Cardiovascular...· 0 citations
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.
Noriko Shiokawa, M. Izumo, Yukio Sato et al.· Journal of Echocardiography· 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
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.
Wojciech Nazar, D. Kaufmann, E. Wabich et al.· Kardiologia polska· 0 citations
Three-dimensional echocardiography compared well with CMR in delineation of severity of regurgitation (mild, moderate, and severe) and intra- and intra-observer reliability was excellent for ventricular volumes, VVR, and RF.
J. Kuebler, M. Moleon-Shea, Minmin Lu et al.· Pediatric Cardiology· 0 citations
FT-CCT applied to clinically indicated CCTA provides a heart rate-robust, reproducible alternative to CMR for LV strain assessment in pediatric myocarditis and its high specificity and diagnostic equivalence support its role as a complementary tool for initial myocardial injury detection and risk stratification when CMR is not immediately available.
