Early Prediction of Fetal Heart Abnormalities in Ultrasound using Artificial Intelligence

  • Research theme/s:

    Healthcare

  • Principal investigator (PI):

    Dr. Mohammad Yaqub

  • Researcher/s:

    Nil

  • Funding:

    MBZUAI

  • Department:

    Computer vision

  • Co-PI:

    Nil

  • Student/s

    Hussain Alasmawi

  • Collaborators / partners:

    Nil

Congenital heart diseases (CHD) are among the most frequent birth defects contributing to around 1 million children a year globally. Ultrasound screening of the fetus is used to acquire different views of the heart that may help detect heart abnormalities. However, human expert detectability of heart defects is erroneous, subjective and time-consuming. Fetal heart is typically checked in the second trimester scan where most of fetal organs can be reviewed. In this work, we aim to develop state-of-the-art machine learning models to classify fetal heart views and check for fetal abnormalities. This shall have a significant effect on supporting clinicians to make more accurate and real-time diagnostic decisions.