Autism spectrum disorder encompasses a broad range of neurodevelopmental variations characterized primarily by challenges in social communication, restricted interests, and repetitive behaviors. One of the greatest challenges clinicians face is the heterogeneity of symptoms and the difficulty in early and precise diagnosis, which significantly impacts long-term outcomes.
Machine learning offers a novel methodology to decode this heterogeneity by analyzing high-dimensional behavioral, genetic, and neuroimaging data. By identifying subtle patterns invisible to conventional statistical techniques, ML models can delineate subtypes within the spectrum, paving the way for more nuanced diagnoses. Machine learning algorithms, including support vector machines, deep neural networks, and random forests, have been progressively applied to extract meaningful biomarkers from neuroimaging data. These integrative models not only improve diagnostic precision but also assist in stratifying individuals for personalized treatment plans that consider unique biological, cognitive, and environmental factors.
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