A Clinically Interpretable Artificial Intelligence System for Real-Time Quality Control of Transthoracic...

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.

To develop, validate, and clinically deploy an interpretable, rules-based AI system for the real-time quality assessment of standard echocardiographic views.

We first designed a novel, quantifiable scoring rubric for 9 standard views, evaluating 4 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 3 publicly available external datasets (n = 1,821 videos).

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.

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 9 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.