NSF Award
2213985
The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to reduce the age of patients diagnosed with autism spectrum disorders (ASD) to approximately six months and produce a detailed picture of where an individual falls on the autism spectrum. These aims will be accomplished via an Artificial Intelligence (AI)-enabled software framework that will analyze brain structure and other medical information from children at risk for ASD. The system may reduce the burden of obtaining a diagnosis of autism, which takes a significant amount of time and thousands of dollars under current clinical practice. The detailed mapping of ASD symptoms to brain regions may help pediatricians and other specialists better communicate their diagnostic findings and inform their plans for treatment. Furthermore, reduction in age at which autism can be diagnosed will give parents and caregivers a greater window of time to apply early intensive behavioral interventions which are known to improve outcomes in autistic children.<br/><br/>The proposed project aims to produce a computer-assisted diagnostic (CAD) system for autism diagnosis based on objective metrics derived from multimodal brain imaging and genomic risk factors. Pediatric autism diagnosis currently relies on subjective evaluations of child behavior. A diagnostic process can begin as early as one or two years of age, but it continues with follow-up observations through age 3–4 years till a final autism diagnosis is rendered. This process can cost $5000–$7000 in total. The proposed CAD system is anticipated to produce a rapid diagnosis at a fraction of current cost. It seeks to identify specific facets of brain anatomy (from structural magnetic resonance imaging [MRI]), and brain connectivity (from functional and diffusion MRI) that will correlate with specific behavioral subtypes of ASD. Evaluating these neurological data in concert with ASD-related variations in the patient’s genome, will produce a detailed profile of brain regions and neural circuit maps implicated in ASD symptomatology. This map will be accomplished using deep machine learning to train the system on a retrospective cohort of high-risk infants, who underwent brain imaging prior to one year of age and were later diagnosed with autism. The system will be validated against an independent dataset.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
