Image quality assessment for closed-loop computer-assisted lung ultrasound

by   Zachary M. C. Baum, et al.

We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models. A quality assessment module automates predictions of image quality, and a diagnosis assistance module determines the likelihood-of-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training a quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Integrating the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at the point-of-care. Using more than 25,000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86 insufficient quality images by the quality assessment module. For data of sufficient quality, the mean classification accuracy in detecting COVID-19-positive cases was 95 the training of any networks within the proposed system.


page 2

page 3


Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering

Ultrasound imaging is a commonly used modality for several diagnostic an...

An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery

In fetal ultrasound screening, Doppler images on the umbilical artery (U...

Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System

Point-of-care ultrasound (POCUS) is one of the most commonly applied too...

Hierarchical Agent-based Reinforcement Learning Framework for Automated Quality Assessment of Fetal Ultrasound Video

Ultrasound is the primary modality to examine fetal growth during pregna...

TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos

Telehealth is an increasingly critical component of the health care ecos...

UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment

Medical image quality assessment (MIQA) is a vital prerequisite in vario...

Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs

In this work, we present an empirical study of DCGANs for synthetic gene...

Please sign up or login with your details

Forgot password? Click here to reset