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Autism spectrum disorder identification using machine learning models on MRI data.

Milner Paul V^1,2, Caren Babu³, Suni Jose⁴

¹Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Manipur, India. vithayathilmilner@gmail.com.

Scientific Reports

|May 29, 2026

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This summary is machine-generated.

This study introduces an objective Autism Spectrum Disorder (ASD) diagnostic method using imaging quality metrics from MRI scans. Machine learning models achieved 95.84% accuracy, demonstrating the potential of technical biomarkers for early ASD detection.

Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Autism Spectrum Disorder (ASD) diagnosis relies on subjective, time-consuming methods.
Early ASD identification is crucial for effective treatment and intervention.
Existing diagnostic approaches lack objectivity and efficiency.

Purpose of the Study:

To develop an objective and efficient diagnostic method for ASD.
To investigate the diagnostic value of technical noise signatures in multimodal MRI data.
To leverage Machine Learning (ML) and Deep Learning (DL) for improved ASD detection.

Main Methods:

Extracted a 'Quality Vector' from multimodal MRI data (sMRI, fMRI, DTI) using Quality Assessment Protocol (QAP) metrics.
Applied Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for outlier removal and Principal Component Analysis (PCA) for dimensionality reduction.

Keywords:

Autism spectrum disorder Convolutional neural network (CNN)Early detection Machine learning Magnetic resonance imaging

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Implemented a classification pipeline including 1D-Residual Network (1D-ResNet), CNN, SVM, kNN, and a Voting Ensemble (SVM, KNN, XGBoost), validated via 10-fold cross-validation.

Main Results:

The proposed Voting Ensemble model achieved the highest diagnostic accuracy of 95.84%.
Technical biomarkers derived from MRI imaging quality metrics proved highly effective for ASD detection.
The Quality Vector framework demonstrated significant diagnostic value from imaging noise.

Conclusions:

Technical quality metrics from MRI scans contain significant diagnostic information for ASD.
The Quality Vector framework offers a computationally efficient and objective tool for ASD identification.
Biomarkers derived from MRI quality metrics show promise for future research in early ASD diagnosis.