Using crowd-powered machine learning to diagnose autism and ADHD in teens through their social interactions
Official title: Crowd-Powered Machine Learning to Diagnose ASD and ADHD in Adolescents from Digital Social Interactions
Funded at University of Hawaii at Manoa · NIH-10682965 · Data updated 2026-11-12
This study is looking to create a new way to help diagnose Autism Spectrum Disorder and ADHD in teenagers by examining how they interact online, making it easier for those who might not have access to regular healthcare to get the support they need.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Hawaii at Manoa NIH-funded |
| Lab location | 1 site (Honolulu, United States) |
| Project start | September 21, 2023 |
| Project end | September 20, 2026 |
| Funding year | 2023 |
| Award (this year) | $1,309,500 |
| NIH project number | 1DP2EB035858-01 |
| Project ID | NIH-10682965 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adolescents who exhibit symptoms of ASD or ADHD and have access to digital platforms for social interaction.
✕Less likely to help
Patients who do not have access to digital technologies or who do not exhibit symptoms of ASD or ADHD may not benefit from this research.
What this research studies
This research aims to develop a new method for diagnosing Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in adolescents by analyzing their digital social interactions. By leveraging crowdsourcing and machine learning algorithms, the project seeks to gather and analyze behavioral data from various digital platforms to identify nuanced social behaviors. This approach aims to improve the accuracy of diagnoses, especially for underserved populations who may lack access to traditional healthcare services. The study will focus on creating detailed representations of social behaviors to differentiate between overlapping neuropsychiatric conditions.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and accessible diagnoses for adolescents with ASD and ADHD, improving their access to appropriate interventions.
How similar studies have gone: While the use of machine learning in psychiatric diagnostics is gaining traction, this specific approach combining crowdsourcing with behavioral analysis is relatively novel and has not been extensively tested.
Where this research is happening
-
University of Hawaii at Manoa
Honolulu, United States Active
Researchers
- Principal investigator: Washington, Peter — University of Hawaii at Manoa
- Study coordinator: Washington, Peter
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.