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Rapid Detection of Neurodevelopmental Phenotypes in Human Neural Precursor Cells NPCs
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Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders.

Omneya Attallah1, Maha A Sharkas1, Heba Gadelkarim1

  • 1Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria 1029, Egypt.

Diagnostics (Basel, Switzerland)
|January 16, 2020
PubMed
Summary

This study introduces a novel deep learning framework for early detection of embryonic neurodevelopmental disorders (ENDs) using MRI scans. The method shows competitive performance, paving the way for improved prenatal diagnostics.

Keywords:
MRI imagingconvolution neural networks (CNNs), machine learningdeep learningembryonic neurodevelopment disorders

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Artificial Intelligence

Background:

  • Neurodevelopmental disorders (NDs) are a growing concern, with some originating during embryonic development.
  • Early detection of embryonic neurodevelopmental disorders (ENDs) is crucial but challenging due to limited research and conventional methods.
  • Existing machine learning approaches rely on handcrafted features, which have inherent limitations.

Purpose of the Study:

  • To propose and evaluate a novel deep learning framework for the detection of embryonic neurodevelopmental disorders (ENDs).
  • To address the limitations of traditional machine learning methods in classifying embryonic brain defects.
  • To establish a new benchmark for END detection using advanced AI techniques.

Main Methods:

  • A four-stage deep learning framework was developed, incorporating transfer learning, deep feature extraction, feature reduction, and classification.
  • The framework utilizes feature fusion to enhance diagnostic accuracy.
  • The model was trained and validated on embryonic MRI images across various gestational ages.

Main Results:

  • The proposed deep learning framework successfully identified ENDs from embryonic MRI images.
  • Performance was evaluated and found to be competitive when compared to existing methods.
  • The framework demonstrated effectiveness in detecting ENDs across different gestational stages.

Conclusions:

  • This study presents the first application of deep learning for END detection, offering a promising advancement in the field.
  • The developed framework provides a robust and effective tool for identifying embryonic brain defects.
  • The findings suggest that this deep learning approach can be successfully implemented for early and accurate END diagnosis.