Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans
This addresses the issue of inconsistent fetal health assessments for sonographers and patients by providing a tool to verify ultrasound image quality, though it is incremental as it applies existing AI methods to a specific clinical domain.
The study tackled the problem of inaccurate gestational age estimation in fetal ultrasound due to improper Crown Rump Length (CRL) view acquisition by developing an AI-based method to assess image quality against 7 clinical scoring criteria, achieving high accuracy compared to an expert and potentially improving diagnosis of conditions like Intrauterine Growth Restriction.
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).