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Updated: Jul 4, 2026
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
Artificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A
Wojciech Nazar1,2, Damian Kaufmann3, Elżbieta Wabich3
11st Department of Cardiology, University Clinical Centre, Gdańsk, Poland. wojciech.nazar@gumed.edu.pl.
Background:
High-quality echocardiography is essential for accurate and reproducible assessmentof cardiac functional indices, which are highly dependent on adequate image quality and properprobe alignment. An artificial intelligence (AI)-based approach may enable automated imagequality assessment.
Aims:
Our aim was to develop and internally validate an AI-based algorithm to predict image qualityfrom selected echocardiographic frames in candidates for implantable cardioverter-defibrillatorimplantation or 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 datawere collected. Apical 2-, 3-, and 4-chamber views were extracted for image quality analysis, yieldinga total of 909 echocardiograms. Image quality was assessed using end-diastolic frames. An internallyvalidated scoring framework was applied, demonstrating high intraclass correlation.
Results:
Regression models provided more clinically relevant information than classificationmodels. Visual Transformer models achieved Pearson correlation coefficients similar to those ofconvolutional neural networks (up to 0.812 and 0.772, respectively; P = 0.31). Architectures trainedon end-diastolic frames achieved comparable Pearson correlation coefficients to those trained oncombined end-diastolic and end-systolic frames. Compared with human experts, the models showedsignificantly 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 validatedAI model can predict an echocardiographic image quality score in a small cohort of candidates forcardiac resynchronization therapy with a defibrillator/implantable cardioverter-defibrillator implantation.However, external and prospective validation will be required to establish its generalizability,reliability, and utility before clinical application.
