Using advanced technology to improve diagnosis of inherited craniofacial diseases
Official title: Multimodal machine learning for diagnosis and mechanistic phenotyping of inherited diseases
Funded at Children's Hosp of Philadelphia · NIH-11089971 · Data updated 2026-11-12
This study is working on a new tool to help doctors quickly and accurately diagnose inherited diseases that show unique facial features, using pictures of faces, medical notes, and genetic information, so that patients and their families can get the answers they need faster.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Children's Hosp of Philadelphia NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project start | June 6, 2025 |
| Project end | June 5, 2026 |
| Funding year | 2025 |
| Award (this year) | $1,156,956 |
| NIH project number | 1OT2OD037960-01 |
| Project ID | NIH-11089971 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include newborns and infants with craniofacial anomalies or suspected genetic syndromes.
✕Less likely to help
Patients without craniofacial abnormalities or those who are older than infancy may not benefit from this research.
What this research studies
This research focuses on enhancing the diagnosis of inherited diseases that present with unique facial features, known as 'facial gestalt.' It aims to develop a multimodal machine learning algorithm that combines facial images, clinical notes, patient demographics, and genetic information to improve the accuracy and speed of diagnosing genetic syndromes. Additionally, the study will utilize advanced imaging techniques to analyze craniofacial morphology and prioritize relevant genes associated with these conditions. By engaging both patients and clinicians in the design process, the research seeks to ensure that the developed tools are practical and applicable in real-world settings.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate diagnoses for patients with craniofacial anomalies, ultimately improving treatment outcomes.
How similar studies have gone: Previous research has shown promise in using machine learning for medical diagnosis, indicating that this approach could be effective.
Where this research is happening
-
Children's Hosp of Philadelphia
Philadelphia, United States Active
Researchers
- Principal investigator: Wang, Kai — Children's Hosp of Philadelphia
- Study coordinator: Wang, Kai
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.