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Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
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First Trimester Noninvasive Prenatal Diagnosis: A Computational Intelligence Approach.

Andreas C Neocleous, Kypros H Nicolaides, Christos N Schizas

    IEEE Journal of Biomedical and Health Informatics
    |August 5, 2015
    PubMed
    Summary

    This study explores using artificial neural networks (ANNs) for noninvasive estimation of chromosomal aneuploidies like trisomy 21 during early pregnancy. Machine learning offers a promising approach to predict fetal aneuploidy risk using ultrasound and biochemical data.

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    Area of Science:

    • Medical Imaging
    • Genetics
    • Artificial Intelligence

    Background:

    • Chromosomal aneuploidies, such as trisomy 21, are significant concerns during early pregnancy.
    • Accurate risk assessment for fetal chromosomal abnormalities is crucial for prenatal care.
    • Current noninvasive methods have limitations in precision and scope.

    Purpose of the Study:

    • To investigate the efficacy of artificial neural network (ANN) schemes for noninvasive estimation of chromosomal aneuploidy risk.
    • To assess the potential of using sonographic and biochemical markers for predicting euploidy, trisomy 21 (T21), and other chromosomal aneuploidies (O.C.A.) between 11-13 weeks of gestation.

    Main Methods:

    • Utilizing machine learning techniques, specifically artificial neural network (ANN) models.
    • Employing a dataset comprising sonographic, biochemical, and other relevant markers.
    • Focusing on data from pregnancies at 11-13 weeks of gestation.

    Main Results:

    • The study aims to demonstrate the feasibility of ANN models in accurately estimating aneuploidy risk.
    • Expected results will highlight the predictive performance of the model using integrated sonographic and biochemical data.
    • The research seeks to validate the potential of noninvasive markers for T21 and O.C.A. detection.

    Conclusions:

    • Artificial neural networks show potential for noninvasive prenatal risk assessment of chromosomal aneuploidies.
    • This approach could enhance early detection of conditions like trisomy 21.
    • Further validation is needed, but machine learning offers a promising avenue for improved prenatal screening.