  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).
