Exploring the structural and strategic bases of autism spectrum disorders with deep learning

Deep learning models are applied in clinical research in order to diagnose disease. However, diagnosing autism spectrum disorders (ASD) remains challenging due to its complex psychiatric symptoms as well as a generally insufficient amount of neurobiological evidence. We investigated the structural a...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Ke, Fengkai, Choi, Seung Jin, Kang, Young Ho, Cheon, Keun-Ah, Lee, Sang Wan
Format: Article
Language:English
Subjects:
Artificial neural networks
Austism Spectrum Disorders
Autism
Biological system modeling
Brain
Brain modeling
Datasets
Deep Learning
Diseases
Disorders
Ganglia
Image classification
Image resolution
Machine learning
Magnetic resonance imaging
Medical imaging
Mental disorders
Neural Networks
Recurrent neural networks
Signs and symptoms
sMRI
Streamlining
Three-dimensional displays
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Summary:Deep learning models are applied in clinical research in order to diagnose disease. However, diagnosing autism spectrum disorders (ASD) remains challenging due to its complex psychiatric symptoms as well as a generally insufficient amount of neurobiological evidence. We investigated the structural and strategic bases of ASD using 14 different types of models, including convolutional and recurrent neural networks. Using an open source autism dataset consisting of more than 1000 MRI scan images and a high-resolution structural MRI dataset, we demonstrated how deep neural networks could be used as tools for diagnosing and analyzing psychiatric disorders. We trained 3D convolutional neural networks to visualize combinations of brain regions, thus representing the most referred-to regions used by the model whilst classifying the images. We also implemented recurrent neural networks to classify the sequence of brain regions efficiently. We found emphatic structural and strategic evidence on which the model heavily relies during the classification process. For instance, we observed that the structural and strategic evidence tends to be associated with subcortical structures, including the basal ganglia (BG). Our work identifies the distinct brain structures that characterize a complex psychiatric disorder while streamlining the deductive reasoning that clinicians can use to ensure an economical and time-efficient diagnosis process.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3016734