Detection of Autism in Infants
The project goal is to develop, demonstrate, and validate an automated, objective system for detecting early warning signs of autism in infants. The approach is non-invasive and uses an in-home system comprising low-cost, off-the shelf equipment in the form of microphones, video cameras, and accelerometers. Data generated by the system are transmitted via internet protocol to a central processing facility where innovative algorithms -- which will be the core contribution of the proposed study -- extract diagnostic profiles. Unlike current diagnosis and detection of autism, which relies on behavioral assessment and subjective clinical judgment along with parent questionnaires, these diagnostic profiles are objective and based on sophisticated computer analysis of voice and movement patterns and hence are expected to be more reliable, accurate, and information-rich.
The prototype system exemplifies an exciting new telemedicine model that may be applicable to a broad range of both neurodevelopmental disorders in addition to autism (e.g., ADHD, child bipolar disorder, ...) as well as neurodegenerative disorders (e.g., Parkinson's, Alzheimer's, ALS, ...). By replacing expensive direct clinical observation with automated data collection, and by providing the experts with highly informative and accurate diagnostic profiles, significant cost savings and simultaneous increases in diagnostic accuracy and accessibility can be expected.
Equally exciting about this project is the wealth of data and the powerful algorithms it will create, which will provide leverage for several future research studies on autism that in turn will lead to new generations of methods for diagnosis and intervention.