A Clinically Interpretable AI System for Real-Time Quality Control of Transthoracic Echocardiography: Development, Validation, and Deployment.
TL;DR
A comprehensive AI system that provides accurate, immediate, and interpretable feedback on echocardiographic quality is successfully developed and clinically deployed, demonstrating strong potential to standardize image acquisition, enhance diagnostic confidence, and improve the efficiency of both clinical practice and sonographer training.
Abstract
Background
Quality control (QC) in echocardiography is crucial but is often subjective, retrospective, and labor-intensive. Artificial intelligence (AI) offers a path to objective, real-time assessment, yet many systems lack clinical interpretability and broad applicability.
Purpose
To develop, validate, and clinically deploy an interpretable, rule-based AI system for the real-time quality assessment of standard echocardiographic views.
Materials And Methods
We first designed a novel, quantifiable scoring rubric for nine standard views, evaluating four key domains: visualized structures, cardiac axis, depth, and gain. This rubric was then automated using a modular AI pipeline, featuring a SlowFast-Echo model for view classification and specialized deep learning models (including SSD and U-Net) for domain-specific assessment. The system was developed on 2,966 videos from a single center, prospectively validated on a temporally distinct cohort of 1,801 videos against an expert-consensus reference standard, and externally validated across three publicly available external datasets(n=1,821 videos).
Results
The view classification model achieved an average accuracy of 98.6%. In classifying overall image quality, the complete AI system demonstrated high agreement with expert consensus, achieving an accuracy of 95.0% on the prospective test set. High performance was maintained across all individual quality domains (accuracy 94.7%-98.7%). The system also showed robust generalizability from 91.7% to 92.3% accuracy across the publicly available external datasets and operated efficiently with a mean inference time of 303 ms per video, confirming its suitability for real-time clinical deployment.
Conclusion
We successfully developed and clinically deployed a comprehensive AI system that provides accurate, immediate, and interpretable feedback on echocardiographic quality. By translating expert criteria into an objective and automated framework for nine standard views, this tool demonstrates strong potential to standardize image acquisition, enhance diagnostic confidence, and improve the efficiency of both clinical practice and sonographer training.