R01LM013364

A Mobile Game for Domain Adaptation and Deep Learning in Autism Healthcare - Project Summary

Neuropsychiatric disorders are the single greatest cause of disability due to non-communicable diseases worldwide, accounting for 14% of the global burden of disease. The current standards of care suffer from subjectivity, inconsistent delivery, and limited access with growing waitlists. New informatics solutions, in particular artificial intelligence (AI) that can port to more ubiquitous mobile health devices and that are not restricted for use in clinical settings, have great potential to complement or even replace aspects of the standards of care.

We propose to develop a novel informatics solution for one of the most pressing mental health burdens, autism, which has increased in incidence by more than 600% since 1990. Autism is among the fastest-growing pediatric concerns today and is highly representative of many other neuropsychiatric conditions. We have invented a prototype mobile system called Guess What (guesswhat.stanford.edu) (GW) that turns the focus of the camera on the child through a fluid social engagement with his/her social partner. This engagement reinforces prosocial learning while simultaneously measuring the child's developmental learning progress.

At its simplest level, the GW app challenges the child to imitate social and emotion-centric prompts shown on the screen of a smartphone held just above the eyes of the individual with whom the child is playing. But more importantly, as a home-based repeat-use system, GW uses computer vision algorithms and emotion classifiers integrated into gameplay to detect emotion in the child's face via the phone's front camera. It automatically finds agreement with the displayed prompt while capturing features such as gaze, eye contact, and joint attention.

Preliminary work with more than 20 autistic children resulted in positive user feedback, evidence of high engagement for both the parents and children, and, importantly, evidence of clinically meaningful gains in socialization. A single session produces 90 seconds of enriched social video and sensor data, opening up an exciting opportunity for the gameplay itself to passively generate labeled computer vision libraries. These libraries enable the development of better models with higher diagnostic precision going forward.

Our proposed project will show that GW can (a) serve as a mobile therapy that can be used repeatedly by families to target core deficits of autism while inherently tracking progress during use, and (b) serve as a distributed system to crowdsource the acquisition of new labeled image libraries for AI models. These models can automatically classify diagnostic features relevant to autism and extend to other sectors of mental health and even beyond.

The Leland Stanford Junior University

TO MEET A GROWING NEED FOR INVESTIGATORS TRAINED IN BIOMEDICAL INFORMATICS RESEARCH AND DATA SCIENCE BY TRAINING QUALIFIED PRE- AND POST-DOCTORAL CANDIDATES, TO CONDUCT RESEARCH IN BIOMEDICAL INFORMATICS, BIOINFORMATICS AND RELATED COMPUTER, INFORMATION AND DATA SCIENCES, TO FACILITATE MANAGEMENT OF ELECTRONIC HEALTH RECORDS AND CLINICAL RESEARCH DATA, TO PREPARE SCHOLARLY WORKS IN BIOMEDICINE AND HEALTH, TO ADVANCE BIOCOMPUTING AND BIOINFORMATICS THROUGH PARTICIPATION IN FEDERAL INITIATIVES RELATING TO BIOMEDICAL INFORMATICS, BIOINFORMATICS AND BIOMEDICAL COMPUTING, AND TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, THROUGH SMALL BUSINESS INNOVATION RESEARCH (SBIR) AND SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) GRANTS.

93.879 - Medical Library Assistance

National Library of Medicine (NLM) [HHS - NIH]

Stanford, California 94305 United States

Single Zip Code

Research Project Grant (Parent R01 Clinical Trial Not Allowed) (PA-19-056)

Amendment Since initial award the total obligations have increased 377% from $667,434 to $3,183,539.