# Novel video-based approaches for detection of autism risk in the first year of life

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $717,517

## Abstract

Signs of autism spectrum disorder (ASD) emerge in the first year of life in many children, but diagnosis is
typically made much later, at an average age of 4 years in the United States. Early intervention is highly
effective for young children with ASD, but is typically reserved for children with a formal diagnosis, making
accurate identification as early as possible imperative. A screening tool that could identify ASD risk during
infancy offers the opportunity for intervention before the full set of symptoms is present. In this application, we
propose two novel video-based methods of detecting ASD in the first year of life. First, we will validate a
recently developed instrument, the Video-referenced Infant Rating System for Autism (VIRSA), in a general
community sample of infants. The VIRSA is a brief web-based instrument that utilizes video depictions rather
than written descriptions of behavior to detect signs of ASD. It leverages thousands of hours of already
collected and hand-coded video obtained through previous NIH funding. Videos demonstrating a continuum of
behaviors and developmental competence are presented to parents, who identify the ones most representative
of their child. Through previous funding, we have established that the VIRSA has good psychometric properties
when used by parents with previous experience of ASD (i.e., have an older affected child) and demonstrated
that it is able to distinguish infants developing ASD in the first year of life. In Aim 1, we will examine the
measure’s use by parents who are naïve to ASD, with no family history of the disorder. In Aim 2, we propose
another innovative method of utilizing video for ASD detection. Machine learning is an application of artificial
intelligence in which computer programs “learn” and adjust themselves in response to training data to which
they are exposed, improving performance and generalization to novel data without being explicitly
programmed. We propose to use the videos from the VIRSA, previously demonstrated in our initial validation
study to be sensitive to early signs of ASD, as training inputs to develop machine-learning algorithms for
automatic detection of ASD-related behaviors. The huge video archive available for this project, with hand-
coded time-stamped behavioral tags, is a highly valuable resource for machine learning. Aim 2 will lay the
foundation for future attempts to develop video-based mobile applications for ASD recognition, which require
validated classifiers that can recognize behavioral events central to early detection of ASD. The ultimate goal
of the two aims of the proposed project is to develop low-cost, low-burden measures that capitalize on new
technologies, including mobile platforms, video, and machine learning methods, to detect ASD risk in infancy.
Such measures would have significant public health applications, including screening large community-based
samples and longitudinally tracking development in pediatric settings to identi...

## Key facts

- **NIH application ID:** 10201443
- **Project number:** 5R01MH121344-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Sally Ozonoff
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $717,517
- **Award type:** 5
- **Project period:** 2019-09-09 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10201443

## Citation

> US National Institutes of Health, RePORTER application 10201443, Novel video-based approaches for detection of autism risk in the first year of life (5R01MH121344-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10201443. Licensed CC0.

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