Deep learning framework for interpretable quality control of echocardiography video

Echocardiography (echo) has become an indispensable tool in modern cardiology, offering real-time imaging that helps clinicians evaluate heart function and identify abnormalities. Despite these advantages, the acquisition of high-quality echo is time-consuming, labor-intensive, and highly subjective.

The objective of this study is to introduce a comprehensive system for the automated quality control (QC) of echo videos. This system focuses on real-time monitoring of key imaging parameters, reducing the variability associated with manual QC processes.

Our multitask network analyzes cardiac cycle integrity, anatomical structures (AS), depth, cardiac axis angle (CAA), and gain. The network consists of a shared convolutional neural network (CNN) backbone for spatial feature extraction, along with three additional modules: (1) a bidirectional long short-term memory (Bi-LSTM) phase analysis (PA) module for detecting cardiac cycles and QC targets; (2) an oriented object detection head for AS analysis and depth/CAA quantification; and (3) a classification head for gain analysis. The model was trained and tested on a dataset of 1331 echo videos. Through model inference, a comprehensive score is generated, offering easily interpretable insights.

The model achieved a mean average precision of 0.962 for AS detection, with PA yielding average frame errors of 1.603 $\pm$ 1.181 (end-diastolic) and 1.681 $\pm$ 1.332 (end-systolic). The gain classification model demonstrated robust performance (Area Under the Curve > 0.98), and the overall processing speed reached 112.4 frames per second. On 203 randomly collected echo videos, the model achieved a kappa coefficient of 0.79 for rating consistency compared to expert evaluations CONCLUSIONS: Given the model's performance on the clinical dataset and its consistency with expert evaluations, our results indicate that the model not only delivers real-time, interpretable quality scores but also demonstrates strong clinical reliability.