# Novel computer vision-based assessment of infant-caregiver synchrony as an early level II screening tool for autism

> **NIH NIH R21** · CHILDREN'S HOSP OF PHILADELPHIA · 2020 · $220,000

## Abstract

PROJECT SUMMARY
This R21 addresses a critical need for accurate and scalable screening tools able to detect autism spectrum
disorder (ASD) within the first year of life. This project will pilot an innovative digital phenotyping screening
method, which uses computer vision and machine learning to measure synchrony within simple infant-caregiver
interactions. Synchrony refers to the tendency for infants to spontaneously and dynamically coordinate their
behaviors with their caregivers in time. This critical and early-emerging developmental process may provide
unique and precise information about an infant’s risk for ASD, while also offering a lens for understanding early
social interaction differences at the core of ASD. Significance: This project represents a paradigm shift in ASD
screening, moving beyond behavior rating scales toward methods that are better suited to capture the subtle
early indicators of ASD. Caregiver rating scales lack the granularity and objectivity necessary for detecting signs
of ASD that emerge slowly and subtly throughout the first year. Approach: The interdisciplinary study team will
leverage cutting-edge technology to objectively and granularly measure synchrony within 5-minute, play-based
infant-caregiver interactions. Markerless computer vision will be used to quantify facial movements, captured
unobtrusively with small, bidirectional cameras. The dyadic synchrony among infants’ and caregivers’ facial
movements will then be calculated throughout the interaction, as part of an automated machine learning pipeline.
Preliminary Data: We evaluated this approach in young adults with and without ASD during brief conversational
interactions with research staff members. In a machine learning analysis pipeline, synchrony features classified
diagnosis with 91% accuracy - significantly better than expert clinicians assessing the same videos. The same
set of synchrony features significantly predicted symptom severity in the ASD group, suggesting that this method
is effective for both diagnostic classification and dimensional prediction of individual differences. Importantly, the
pipeline also classified diagnosis in children with similarly high accuracy, demonstrating the reproducibility of
results across age groups. Aims. This project extends these computer vision-based methods to infants, with the
overarching goal of evaluating their utility as a Level II screener for ASD. Aim 1 will evaluate the concurrent
validity of our computational measures of interactional synchrony by evaluating their relationships with an
established clinician-administered assessment of early ASD markers. Aim 2 will assess the utility of our
interactional synchrony measure as a Level II screening tool at 12 months, by testing its ability to predict future
ASD diagnosis with high specificity. Impact: This R21 will provide initial validation for a novel, computer vision-
based screener for ASD in infancy. By targeting the dynamics of natural infant-caregive...

## Key facts

- **NIH application ID:** 10023938
- **Project number:** 5R21HD102078-02
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** ROBERT Thomas SCHULTZ
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $220,000
- **Award type:** 5
- **Project period:** 2019-09-24 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10023938

## Citation

> US National Institutes of Health, RePORTER application 10023938, Novel computer vision-based assessment of infant-caregiver synchrony as an early level II screening tool for autism (5R21HD102078-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10023938. Licensed CC0.

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