July 5, 2026Open Access
View Full PaperArtificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A proof-of-concept study
Key Result
AI-based models for predicting echocardiographic image quality showed higher absolute percentage errors compared to human experts (median 11%-12% vs. mean 7.9%).
Key Points
- The study aims to develop and validate an AI algorithm to predict echocardiographic image quality in candidates for cardiac devices.
- Retrospective cross-sectional study with 248 patients and 297 echocardiograms.
- Demographic, clinical, and echocardiographic data were collected for analysis.
- Image quality was assessed using an internally validated scoring framework on apical views.
- Regression models provided more clinically relevant information than classification models.
- Visual transformer models achieved Pearson correlation coefficients up to 0.812, while convolutional neural networks reached 0.772; P=0.31.
- Models showed higher absolute percentage errors compared to human experts: 11%-12% median vs. 7.9% mean for total image quality.
Study Design
Type
Cross-Sectional (n=248)
Structured PICO
Does an artificial intelligence-based algorithm accurately predict transthoracic echocardiographic image quality compared to human experts in ICD/CRT-D candidates?
P
Population
248 candidates for implantable cardioverter-defibrillator and cardiac resynchronization therapy with a defibrillator implantation.
E
Exposure
Artificial intelligence (AI)-based algorithm (including visual transformer models and convolutional neural networks) to predict image quality from selected echocardiographic frames.
C
Comparator
Human experts
O
Outcome
Echocardiographic image quality score (measured via Pearson correlation coefficients and absolute percentage errors)surrogate
An AI-based algorithm can predict echocardiographic image quality in ICD/CRT-D candidates, though currently with higher error rates than human experts, providing a proof-of-concept for automated quality assessment.
Limitations
- External and prospective validation will be required to establish its generalizability, reliability, and utility before clinical application
Abstract
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.