A-System

Autism Spectrum Disorder Diagnosis and Treatment System

Group Publication

2021 Summer

Personal Designed System

2022 Spring

Role:

Group Leader, Data Visualization, Product Design, UI/UX

Tools:

Python, Fusion 360, SolidWork, Adobe Xd

Advisor:

Manolis Kellis

Recognition:

Published in the 4th International Conference on Computing and Data Science CONF- CDS (2022)

Machine Learning Early Stage

Clinical Diagnosis

Independent Emotion

Regulation Technology

UIUX System Interface

Autism spectrum disorder (ASD) is a neurodevelopment disorder difficult to diagnose and recover. In addition to the ASD patient being affected, there are several other stakeholders will impact the disease including school, family and hospital. A-System is designed to create a coherent network dedicate for the ASD children's diagnose accuracy to 75% and a better independent emotion regulation treatment through assistive technology.

Background

In each main scenario for the ASD children, there are different painpoints with various stakeholders such as: specialists, doctors, teachers, parents and children themselves. Different stakeholders will have the timely relationship in order to ensure a fair treatment overall.

Painpoints

Scenario

Stakeholder map

Machine Learning Early Stage
Clinical Diagnosis

The lack of diagnosis in early stages cause the ASD treatment to be harder. The current psychiatric diagnostic process is based simply on observing the behavioral symptoms, nor suitable for quantitative diagnosis and may lead to false diagnosis. Advances in neuroimaging technologies have made it easy to measure those pathological changes related to the brain with autism spectrum disorder. Nevertheless, the differences between the brain with ASD and the healthy controls difficult to apply.

Introduce the abnormality in ASD brain images

(A) Structural covariance patterns are poorly connected in autism

(B) Corresponding MRI map in normal controls, connectivity marked in cool colors

Statistical parametric maps depict brain regions in which gray matter intensity covaried with the Regions of Interests (2012 Zielinski et al)

ABIDE Dataset with Sufficient fMRI Data for Early Age Group

To face the complexity and heterogeneity of ASD, large-scale samples are essential. The ABIDE initiative has aggregated brain imaging data collected from laboratories around the world. In the MRI data, different brain volumes are represented by voxels and the activity is tracked over time:

539 individuals
ages 7-64 years
median 14.7 years
573 typical controls

1. Feature Extraction:
Increases the Accuracy of Learned Models by Diagonal Symmetry from the Input

The first flowchart illustrates the process of the feature extraction. Calculate all pairwise Spearman correlation to approximate the functional connectivity in fMRI data to generate a matrix. The generated matrix calculated is diagonal symmetric, the matrix’s upper triangle transform into a one-dimensional vector as the features of Autoencoder.

2. Data Augmentation:
Mixup Reduced Insufficient Training

The dataset applied in the study is preprocessed by a pipeline called C-PAC (Configurable Pipeline for the Analysis of Connectomes), which parcellated the brain into 200 functionally homogeneous regions using a spatially constrained spectral clustering algorithm (CC-200). To reduce the impact of the insufficient training set on the generalization ability of the model, this work uses a linear data augmentation method called ‘Mixup’ to expand our dataset

3.1. Sparse Autoencoder with Deep Neural Network: Prevent Overfitting Data and Improve Generalization

The benefit of sparse autoencoder is that the model can acquire finer representations and activations are sparser, preventing the data from overfitting. A rectified linear unit (ReLU) activate the units expanding the network with more representative and improved generalization.

3.2. Variational Autoencoder with Deep Neural Network: Reduce Parameters and Network Errors

Variational autoencoders with objective Kullback-Leibler Divergence (KL divergence) mapped input to distribution and the bottleneck vector is replaced by two various vectors including the standard deviation and the mean of the distribution. This reduce the generative modal parameters and reconstruction error within the network.

4. Cross Validation: Model Performance Evaluation

To test model performance, adapted the model validation method K-folding cross validation. The original data is divided into K folds for each individual site and for all the sites together.

Calculation of Result:
Individual Sites Result Different from the Sites as a Whole

The ABIDE dataset, including 1035 subjects, has patients from various sites. Therefore, the result is summarized from both individual sites and the generalization across different data acquisition sites.

Compare with Other Methods:
Mixup+SAE+DNN with Highest Accuracy and Sensitivity

In the proposed experiment, there are two different approaches named method 3.1 Mixup +SAE+DNN and 3.2. Mixup+VAE+DNN. Different performance compared to the past models through the three significant values Accuracy, Sensitivity and Specificity.

Accuracy: correctly classified subjects.
Sensitivity: accurately considered carrying the ASD.
Specificity: successfully classified healthy subjects.

Independent Emotion Regulation Technology

In addition to the accurate clinical diagnosis from the brain imaging, the ASD children often suffer from the symptoms of hypersensitivity. The goal is to train the children in order to be more independent and regulate the emotion by themselves which could benefit them when they get older.

Persona in Journey Map

ASD student
Age: 8
Hyper-sensitivity: light and noise

Jason is a student in the ASD Special school and had been diagnosis ASD with sensory processing disorder.

How can child cope with the daily activities that happens in the school?

System User Interface

After the development of the early stage clinical diagnosis and the product design of the assistive technology. Now it is the time to combine all the stakeholder in one system interface to contribute to the better treatment and independent emotion regulation of the ASD children.