Novel Digital Diagnosis of Autism
eXPLORING AlternativeS during covid-19
Presenter Information
Hello! My name is Emilin Mathew and I am a junior at American Heritage High School. I'm passionate about psychiatric research and its intersection with public health policy.
Outside of science, I'm involved in Lincoln Douglas Debate, Model United Nations, Womxn in Debate, and a nuclear policy making group. I also lead a national indigenous outreach program that aims to spread indigenous education and increase minority representation in public policy.
Short Summary of My Research: During COVID-19, psychologists have raised concerns on how effectively autism diagnoses can evaluate social behavior in this new environment (children wearing masks, staying distanced, etc). Additionally, the gold standard for the autism diagnosis has numerous faults such as not being objective and requiring costly training for clinicians. The purpose of my research was to use computer vision analysis with motion tracking technology and vocalization analysis to test whether movement and vocalization could become new predictive determiners for autism severity.
Abstract
Digital Phenotyping Autism: Investigating Objective Vocal and Movement Quantification for Characterizing Autism Severity
Autism Spectrum Disorder is a neurodevelopmental disorder that lacks objective diagnostics. The current gold standard, Autism Diagnostic Observation Schedule (ADOS), suffers from problems such as subjective evaluations of clinicians, inability to distinguish symptom severities, and poor early detection because it only evaluates existing conditions of children. Additionally, COVID-19 severely affects this diagnostic because it prevents the close social interaction necessary to observe children's behavior. As the pandemic disrupts autism diagnoses, new methods are needed. This research used computer vision motion tracking technology to assess mean distances of autistic children from parent and examiner during the ADOS and vocalization recorders to analyze children’s language ability. Both predictive measures (movement and vocalization) were analyzed for correlations with the total ADOS score, Restricted and Repetitive Behavior score, and Social Affect score to characterize autism severity. Using univariate correlations and stepwise multiple regressions, the researcher identified significant models to quantify autism severity. The vocalization hypothesis, which predicted the number of conversational exchanges (CE) initiated by the child to be correlated with the social affect score, was supported and produced the best fit model accounting for 16.1% variance. It predicted that a decrease in child-initiated CEs would correlate to an increase in autism social affect symptoms. Overall, this study provides a foundation for automating autism diagnosis through digital phenotyping which may be integrated into smartphone apps. This easy and portable method can provide real time data in natural environments and revolutionize autism diagnosis, especially in the crisis of COVID-19.
