ArticleMedical physics2025
Deep learning framework for interpretable quality control of echocardiography video.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Beyond Computer-Aided Diagnosis: Artificial Intelligence as a "Digital Mentor" for POCUS Image Acquisition and Quality Assurance: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning framework for interpretable quality control of echocardiography video.Medical physics · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundEchocardiography (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. PURPOSE: 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.
methodsOur 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.
resultsThe model achieved a mean average precision of 0.962 for AS detection, with PA yielding average frame errors of 1.603
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.