Effectiveness of ML with Neuroimaging Data in Detecting Individuals/Children with ASD

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting one in thirty-six children. It often emerges early but is diagnosed later, making early detection crucial for not only improving quality of life but also reducing lifetime care costs. The current diagnostic methods in India, relying primarily on behavioural observations, lack the precision and early detection capabilities needed. This paper explores the use of non-intrusive neuroimaging techniques, specifically structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) combined with machine learning (ML) for early ASD detection in India. Western nations usually conduct these neuroimaging investigations, identifying structural and functional brain abnormalities such as increased brain volume and altered white matter integrity in people with ASD. However, traditional neuroimaging analysis is labour intensive and prone to human error. In contrast, medical practitioners in India rely more on their tacit knowledge and intuitive intelligence for ASD diagnosis rather than neuroimaging. This research makes use of a Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyse feedback from medical practitioners to a questionnaire focused on the integration of ML and neuroimaging for ASD diagnosis in India. It uses a 5-point Likert scale to answer the research questions leading to the conclusion, common challenges, as well as recommendations on the future steps. We conclude that ML-based diagnostic tools, through its adaptation and application for the Indian context, will assist medical practitioners in diagnosing ASD in India.