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Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans
  • Sevim Cengiz, Mohamed bin Zayed University of Artificial Intelligence
  • Mohammad Yaqub, Mohamed bin Zayed University of Artificial Intelligence
Document Type

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). © 2022, CC BY-NC-SA.

Publication Date
  • Image acquisition; Ultrasonics; Age estimation; Crown-rump length; Deep learning; Fetal growth; Fetal ultrasound; Gestational age; Gestational age estimation; Quality assessment; Scoring criteria; Sonographers; Deep learning; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC BY-NC-SA 4.0

Uploaded 25 March 2022

Citation Information
S. Cengiz, and M. Yaqub, "Deep learning-based quality assessment of clinical protocol adherence in fetal ultrasound dating scans", 2022, arXiv:2201.06406