Scientists have developed and tested a deep-learning model that could support clinicians by providing accurate results and clear, explainable insights - including a model-estimated probability score for autism.
The model, outlined in a study published in eClinicalMedicine (a journal from The Lancet), was used to analyse resting-state fMRI data - a non-invasive method that indirectly reflects brain activity via blood-oxygenation changes.
In doing so, the model achieved up to 98% cross-validated accuracy for Autism Spectrum Disorder (ASD) and neurotypical classification and produced clear, explainable maps of the brain regions most influential to its decisions.
ASD diagnoses have increased substantially over the past two decades, partly reflecting greater awareness, expanded screening, and changes to diagnostic criteria and clinical practice. Early identification and access to evidence-based support can improve developmental and adaptive outcomes and may enhance quality of life, though effects vary.
However, because the current diagnosis primarily relies on in-person and behavioural assessments - and the wait for a confirmed diagnosis can stretch from many months to several years - there is an urgent need to improve assessment pathways.
The researchers hope that, with further validation, their model could benefit autistic people and the clinicians who assess and support them by providing accurate, explainable insights to inform decisions.
The study was the result of a final-year undergraduate project by BSc (Hons) Computer Science student Suryansh Vidya, supervised by
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