Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review (2007.01285v4)

Abstract: Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. AI techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional ML approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.

• The paper demonstrates that deep learning enhances ASD diagnosis by automating feature extraction from MRI and EEG data.
• The review evaluates DL architectures—including CNNs, RNNs, and autoencoders—to improve classification and capture temporal dependencies.
• The study highlights promising DL-based rehabilitation tools that enable real-time monitoring and enhance patient engagement in clinical settings.

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

This review paper comprehensively evaluates the application of deep learning (DL) techniques in the domain of neuroimaging for diagnosing and rehabilitating individuals with Autism Spectrum Disorder (ASD). ASD, a neurodevelopmental disorder characterized by challenges in social interaction, communication difficulties, and repetitive behaviors, affects a significant portion of the global population. Diagnosing ASD is notoriously difficult due to the absence of pathophysiological markers, necessitating a reliance on psychological assessments. The integration of artificial intelligence, particularly DL, in neuroimaging presents a promising avenue for automated and more accurate ASD diagnosis and rehabilitation.

Diagnostic Framework

The paper details how DL methods are applied to structural and functional neuroimaging data to facilitate the diagnosis of ASD. Conventional machine learning approaches have been utilized in this context; however, DL presents unique advantages by automating feature extraction and classification, thus enhancing diagnostic accuracy. The primary neuroimaging modalities discussed include MRI (sMRI and fMRI) and EEG, with MRI techniques providing insights into both structural and functional brain abnormalities associated with ASD.

Deep Learning Architectures

Several DL architectures are evaluated within the context of ASD diagnosis:

1. Convolutional Neural Networks (CNNs): The review highlights the successful use of CNNs, particularly 3D CNNs for processing volumetric data from MRIs. CNNs excel in spatial feature extraction, making them suitable for analyzing complex neuroimaging data.
2. Recurrent Neural Networks (RNNs) and LSTM Networks: These networks are employed to capture temporal dependencies in the data, relevant in fMRI studies where time-series analysis is crucial.
3. Autoencoders (AEs) and Variational Autoencoders (VAEs): These are leveraged for unsupervised feature learning, which is essential when labeled data is scarce. AEs can provide meaningful feature representations that enhance classification tasks.
4. Multimodal Approaches: The integration of multiple data types, such as combining sMRI with fMRI, is explored to improve the robustness of ASD diagnostic systems.

Rehabilitation Tools

In addition to diagnostic applications, the paper reviews DL-based rehabilitation tools aimed at aiding ASD patients. These tools include mobile and software applications for emotion recognition and interaction enhancement, cloud systems for real-time monitoring, and eye tracking technologies to paper and support engagement.

Performance and Challenges

The effectiveness of DL models in diagnosing ASD is quantitatively demonstrated with performance metrics like accuracy, sensitivity, and specificity. Notably, some studies report high accuracy rates, indicating the potential of DL models to surpass conventional diagnostic methods in certain cases. However, the field faces significant challenges, particularly concerning data availability. Large and diverse datasets are necessary for training accurate and generalizable DL models, yet such datasets are currently limited.

Conclusions and Future Directions

The paper suggests that while DL exhibits significant promise in both diagnosis and rehabilitation of ASD, continued research is imperative. Future directions include the incorporation of reinforcement learning and generative models like GANs to counteract data scarcity and enhance model robustness. Developing low-power, portable diagnostic and rehabilitation tools remains an overarching goal, ensuring accessibility and practical application in clinical settings.

This review underscores the potential of DL in transforming ASD diagnosis and rehabilitation, inviting further interdisciplinary collaboration to harness these advanced techniques effectively.