Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans

TL;DR

This paper introduces an AI-based system to evaluate the quality of fetal ultrasound images by verifying clinical criteria, aiming to improve gestational age estimation accuracy and assist sonographers in acquiring better images.

Contribution

The study presents a novel deep learning approach for automatic quality assessment of fetal ultrasound images based on clinical scoring criteria.

Findings

High accuracy in scoring criteria verification

Effective identification of poorly acquired images

Potential to improve fetal growth assessments

Abstract

To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory. However, GA estimation based on CRL, requires proper positioning of calipers on the fetal crown and rump view, which is not always an easy plane to find, especially for an inexperienced sonographer. Finding a slightly oblique view from the true CRL view could lead to a different CRL value and therefore incorrect estimation of GA. This study presents an AI-based method for a quality assessment of the CRL view by verifying 7 clinical scoring criteria that are used to verify the correctness of the acquired plane. We show how our proposed solution achieves high accuracy on the majority of the scoring criteria when compared to an expert. We also show that if such scoring system is used, it helps identify poorly acquired images accurately and hence may help sonographers acquire better images which could potentially lead to a better assessment of conditions such as Intrauterine Growth Restriction (IUGR).