ML-VIRSA: Machine-Learning based Video Screening for Detecting Autism Risk in the First Year of Life

Over the last two decades, the average age of first diagnosis of autism spectrum disorder (ASD) in the United States has remained steady, at over 4 years of age, despite an average age of first parental concerns of 14 months and recent progress in understanding manifestations of ASD in infancy. Early intensive intervention has been shown to be highly promising for young children with ASD, including infants and toddlers, but is typically reserved for children with a formal diagnosis, making accurate identification as early as possible imperative. A measure that could identify ASD risk during this period of onset offers the opportunity for intervention before the full set of symptoms is present.

PI Ozonoff and her team have developed a new video-based screening tool, the Video-referenced Infant Rating System for Autism (VIRSA), that utilizes a large library of video clips depicting a wide range of social-communication ability and relies solely on video in the ratings, with no written descriptions of behavior. We hypothesized that the semantic clarity afforded by video would provide improved early discrimination of infants at highest risk for ASD. The VIRSA was able to predict ASD diagnosis with high sensitivity across ages (6-18 months) and demonstrated 100% sensitivity (no false negatives) in concurrently identifying children showing signs of autism at 18 months of age.

Despite the demonstrated success of video-based screening, one major obstacle is the labor-intensive process of labeling and reviewing the videos manually by clinicians. In this proposed collaborative project, we propose to leverage videos from the VIRSA as a training set and apply computer vision and machine learning methods to develop a neural network model for ASD recognition

People

Faculty

Sally Ozonoff, Psychiatry (Principal Investigator)
Gregory S. Young, Psychiatry (Co-Investigator)
Samson Cheung, Electrical & Computer Eng., Univ. of Kentucky (Co-Investigator)
Chen-Nee Chuah, Electrical & Computer Eng. (Co-Investigator)

Graduate Students

Chongruo Wu, CS, UC Davis (PhD)
Sidrah Liaqat, ECE, Univ. of Kentucky (PhD)
Dev Kashikar, ECE, UC Davis (MS)

Publications

C. Wu, S. Liaqat, S. Cheung, C-N. Chuah, and S. Ozonoff, “Predicting Autism Diagnosis Using Image with Fixations and Synthetic Saccade Patterns,” IEEE International Conference on Multimedia and Expo (ICMEW) Grand Challenge, July 2019.

Funding