Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD’s pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML’s general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders

“…ML has been effectively applied in the medical field to diagnose neurological disorders, including ASD (Vakadkar et al, 2021 ; Bahathiq et al, 2022 ; Briguglio et al, 2023 ) and ADHD (Slobodin et al, 2020 ; Mikolas et al, 2022 ; Briguglio et al, 2023 ; Kim et al, 2023 ). These studies have demonstrated the potential of ML to increase diagnostic accuracy, reduce time to diagnosis and improve reproducibility.…”

“…Machine learning (ML) algorithms have demonstrated impressive capabilities in analyzing complex datasets and extracting meaningful patterns in other diseases such as autism spectrum disorder (ASD) or ADHD (Eslami et al, 2021 ; Bahathiq et al, 2022 ; Ehrig et al, 2023 ). By harnessing the power of computational algorithms, researchers can integrate diverse data sources, including physical and cognitive variables, to develop predictive models for FASD diagnosis (Blanck-Lubarsch et al, 2022 ; Ehrig et al, 2023 ).…”

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes

“…For example, ML using brain magnetic resonance imaging scans 19 and brain magnetoencephalography 20 to label ASD cases showed overall lower performance across all metrics. A review of 5 years of ML research using brain imaging to diagnose ASD 21 showed an increasing number of projects. They all suggest the existence of abnormalities, but none could replace clinical assessment.…”

A hybrid CNN-SVM model for enhanced autism diagnosis

“…While other diagnostic tools such as the Autism Diagnostic Observation Schedule, the Childhood Autism Rating Scale, and the Autism Diagnostic Interview-Revised are commonly employed in clinical practice, these methods are more time-consuming. However, these methods are inevitably affected by clinical training, tools, and cultural background, thereby interfering with clinicians’ subjective observations [ 24 , 25 ]. To overcome this limitation, it is not enough to only use the scale results to assist diagnosis.…”