Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis - Verifying White Matter Associations with ASD

Aug 3, 2026

Autism Spectrum Disorder is a neurodevelopmental condition marked by impairments in social skills, communication, repetitive behaviors, and sensory processing. This work develops a computer-aided diagnostic model that targets white matter regions on brain MRI to improve objective ASD identification. A MultiUNet segmentation framework is trained using manually labeled MRI, followed by Pyradiomics feature extraction and classical machine learning classifiers. Additional analysis uses a convolutional neural network on segmented images. Reported accuracies exceed 80%, with SVM achieving the best performance, supporting links between white matter abnormalities and ASD.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Abstract

Keywords

Introduction

Autism spectrum disorder background and diagnostic challenges

Need for early objective diagnosis

Role of neuroimaging and MRI

Machine learning approaches in medical imaging for ASD

Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD Junlin Song1\#, Yuzhuo Chen 1\# , Yuan Yao3\#, Zetong Chen1 , Renhao Guo1, Lida Yang1, Xinyi Sui 1, Qihang Wang1 , Xijiao Li 1, Aihua Cao2* , Wei Li 1* 1 School of Control Science and Engineering, Shandong University, Jinan City, Shandong Province, 250061, China 2 Department of Pediatric, Qilu hospital of Shandong University, Jinan City, Shandong Province, 250012, China 3 Department of Radiology, Qilu Hospital of Shandong University, Jinan City, Shandong Province, 250012, China \#These authors contributed to the work equally and should be regarded as co-first authors. *Corresponding authors: Wei Li, Aihua Cao Wei Li School of Control Science and Engineering, Shandong University Address: Qianfoshan Campus, Shandong University, 17923 Jingshi Road, Jinan City, Shandong Province, 250061, China Telephone number: +86-15153150160 E-mail: [cindy@sdu.edu.cn](cindy@sdu.edu.cn) Aihua Cao Cheeloo College of Medicine, Shandong University, No. 44, Wenhua West Road, Lixia District, Jinan City, Shandong Province, 250012, China Address: Qilu hospital of Shandong University, Jinan City, Shandong Province, 250012, China Shandong Province, 250012, China Telephone number: +86-018560086317 [E-mail: xinercah@163.com](E-mail: xinercah@163.com) Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD Abstract Autism Spectrum Disorder is a condition characterized by a typical brain development leading to impairments in social skills, communication abilities, repetitive behaviors, and sensory processing. There have been many studies combining brain MRI images with machine learning algorithms to achieve objective diagnosis of autism, but the correlation between white matter and autism has not been fully utilized. To address this gap, we develop a computer-aided diagnostic model focusing on white matter regions in brain MRI by employing radiomics and machine learning methods. This study introduced a MultiUNet model for segmenting white matter, leveraging the UNet architecture and utilizing manually segmented MRI images as the training data. Subsequently, we extracted white matter features using the Pyradiomics toolkit and applied different machine learning models such as Support Vector Machine, Random Forest, Logistic Regression, and K-Nearest Neighbors to predict autism. The prediction sets all exceeded 80% accuracy. Additionally, we employed Convolutional Neural Network to analyze segmented white matter images, achieving a prediction accuracy of 86.84% . Notably, Support Vector Machine demonstrated the highest prediction accuracy at 89.47% . These findings not only underscore the efficacy of the models but also establish a link between white matter abnormalities and autism. Our study contributes to a comprehensive evaluation of various diagnostic models for autism and introduces a computer-aided diagnostic algorithm for early and objective autism diagnosis based on MRI white matter regions. Keywords ASD; MRI; Machine Learning; radiomics; White Matter 1.Introduction Autism Spectrum Disorder (ASD) is a developmental condition characterized by deficits in social skills and restricted and repetitive behaviors, interests, or activity patterns that may persist throughout an individual's life[1] . The prevalence of ASD in the United States is currently estimated at 1 out of 36 individuals[2] . The exact cause of ASD remains unknown, with the majority of cases believed to result from a complex interplay of genetic and environmental factors. The intricate nature of ASD, coupled with limited understanding of its underlying causes and biochemical abnormalities, presents challenges in both diagnosis and treatment, making it a global concern[3][4][5] . While there is no specific pharmaceutical cure for ASD, early diagnosi

FAQs

What problem does the study address for objective ASD diagnosis?

The study targets the gap that white matter–ASD correlations have not been fully leveraged in existing MRI-based machine learning approaches, aiming to support objective diagnostic prediction using white matter regions.

How is white matter on MRI processed in the proposed pipeline?

A MultiUNet model segments white matter using the UNet architecture and manually segmented MRI images as training data.

Which modeling methods are used to predict autism and what performance is reported?

Radiomics features from Pyradiomics are classified using SVM, Random Forest, Logistic Regression, and K-Nearest Neighbors, and a convolutional neural network is also applied to segmented images; reported prediction accuracy exceeds 80%, with SVM reaching 89.47%.

Aug 3, 2026

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