Quantitative & Objective Tool for Diagnosing & Monitoring Autism Rutgers co-inventor: Elizabeth B. Torres, Ph.D. Elizabeth B. Torres is an Associate Professor of Psychology at Rutgers University. She joined Rutgers in 2008 upon completion of post-doctoral training in Computational Neural Systems at CALTECH. She holds a PhD in Cognitive Science and a B.Sc. in Mathematics and Computer Science.
Innovation Summary: Autism spectrum disorder (ASD) refers to a group of complex neurodevelopment disorders, including a wide range of symptoms, skills, and levels of disability. Due to the intrinsic complexity, it is very challenging for healthcare professionals to accurately diagnose the disorder and evaluate therapy effectiveness. The current assessment tools such as Autism Diagnostic Interview, Childhood Autism Rating Scale, and Autism Diagnostic Observation Schedule, are based on observations and interviewing, and thus are subjective and very time consuming. Misdiagnosis and late diagnosis are common. Additionally, because payers have difficulty in tracking the effectiveness of a treatment, obtaining reimbursement for certain interventions is extremely challenging. Dr. Torres and collaborators have developed a proprietary algorithm and analytical tool to dynamically classify and diagnose ASD and other neurological disorders as the disorder progresses over time. The methods are based on natural biorhythms output by the nervous systems. These include motions and other physiological parameters (such as heart rate, temperature, electro-encephalography, etc.) tractable using non-invasive means such as wearable sensing technology. Market Applications: Analytic tool for •
diagnosing ASD and other neurological disorders (including, Parkinson’s Disease, ADHD, etc.)
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Tracking and assessing a patient’s daily progression and effectiveness of interventions
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Enabling the design of personalized intervention plans for patients
A multi-modal interface integrates audio-visual inputs and motion caption to engage individuals with autism in selfcontrolled media-driven interactions with and avatar.
Intellectual Property Status: US Patent application #14/354,796, 15/457,634, & PCT/US15/64440
Advantages: • • •
Highly objective and quantitative for dynamic tracking Personalized for each individual’s motion and other physiological parameters To be combined to non-invasive devices (such as cell phones, wearable sensors, & smart watches, etc.) that are suitable for daily use
Potential Social and Economic Impact: This technology has the potential to revolutionize the field of Autism diagnosis and treatment, by replacing the current static, discrete observationbased practices with a dynamic, continuous approach. • According to CDC, ASD affects 1 in 68 children and more than 3.5 million Americans live with ASD. • Current diagnostic systems do not consider the physiological underpinnings of nervous systems disorders • There are no standardized scales characterizing normal development so there is no reference point to characterize the development of a coping system with ASD • Commercially available wearables can provide non-invasive means to overcome current limitations in diagnoses • Using proper analytics, they can become a tool to provide outcome measures of treatment effectiveness or risk Next R&D Steps: • Characterization of thousands of neurotypical cases to provide normative scales for the first time • Characterization of thousands of cases with ASD from open access data repositories in relation to normative data • Use of biometrics to track longitudinal rates of change Outcome measurement in a FDA-approved clinical trial for Phelan McDermid Syndrome: http://journal.frontiersin.org/article/10.3389/fnint.2016.00022/full TIME magazine coverage: http://healthland.time.com/2013/07/24/usingmovement-to-diagnose-and-treat-autism/
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