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Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting

Emel Koc1, Habil Kalkan2, Semih Bilgen1

  • 1Istanbul Okan University, Istanbul, Türkiye.

Autism Research and Treatment
|December 28, 2023
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Summary
This summary is machine-generated.

This study enhances autism spectrum disorder (ASD) diagnosis using machine learning and neuroimaging. Hybrid neural networks achieved 96% accuracy by combining structural and functional MRI data.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Autism spectrum disorder (ASD) diagnosis relies on behavioral and cognitive assessments.
  • Accurate diagnosis is crucial for early intervention and effective treatment.
  • Neuroimaging offers objective biomarkers for understanding brain differences in ASD.

Purpose of the Study:

  • To improve the accuracy of autism spectrum disorder (ASD) diagnosis.
  • To leverage multiple neuroimaging modalities and machine learning for classification.
  • To identify key brain connectivity features associated with ASD.

Main Methods:

  • Utilized structural MRI (s-MRI) and resting-state functional MRI (rs-f-MRI) data from the ABIDE repository.
  • Applied machine learning algorithms, including hybrid convolutional recurrent neural networks (CNNs/RNNs).
  • Employed early, late, and cross-fusion strategies to integrate multimodal imaging data.

Main Results:

  • Hybrid CNN-RNN models outperformed individual CNN or RNN models.
  • Achieved a diagnostic accuracy of 96% by fusing s-MRI and rs-f-MRI data.
  • Identified significant functional and anatomical connectivity metrics for ASD classification.

Conclusions:

  • Multimodal neuroimaging combined with advanced ML significantly enhances ASD diagnostic accuracy.
  • Hybrid neural networks offer a promising approach for objective ASD diagnosis.
  • This method provides a more robust diagnostic tool compared to previous approaches.