Automatic quality assessment of First Trimester Crown-Rump-Length Ultrasound Images

  • Research theme/s:

    Healthcare

  • Principal investigator (PI):

    Dr. Mohammad Yaqub

  • Researcher/s:

    Sevim Cengiz

  • Funding:

    MBZUAI

  • Department:

    Computer vision

  • Co-PI:

    Nil

  • Student/s

    Nil

  • Collaborators / partners:

    SEHA

Fetal gestational age (GA) is a vital clinical information that is estimated during pregnancy in order to assess fetal growth. This is usually performed by measuring the crown-rump length (CRL) on an ultrasound image in the dating scan which is then correlated with fetal age and growth trajectory. Although clinical guidelines specify the criteria for the correct CRL view, sonographers may not regularly adhere to such rules. In this work, we propose a new deep learning-based solution that is able to verify the adherence of a CRL image to clinical guidelines in order to assess image quality and facilitate the accurate estimation of GA. This shall be evaluated on real-world scans and if successful, it may have a significant impact on how sonographers acquire dating scans.