DeepStroke+: a mobile AI app to quickly spot strokes in ambulances, ERs, and remote stroke consults
DeepStroke+: An Advanced Mobile AI Diagnostic Tool for Fast and Precise Detection of Acute Strokes in Mobile Stroke Units, Emergency Rooms, and Telestroke Triage
This project will create and test an AI-powered app that uses short facial videos and speech samples to help emergency teams more quickly spot strokes in people with sudden neurological symptoms.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Methodist Hospital Research Institute NIH-funded |
| Lab location | 1 site (Houston, United States) |
| Project ID | NIH-11269162 on NIH RePORTER |
What this research studies
From a patient perspective, researchers will ask English-speaking patients with possible stroke to describe a picture while a short facial video and audio are recorded. The team will train an AI called DeepStroke+ to recognize speech and facial patterns that suggest ischemic stroke, hemorrhage, or transient ischemic attack versus stroke mimics. The app will be deployed in mobile stroke units, emergency departments, and telestroke consultations and tested in those real-world triage situations. Study staff will compare the app’s triage suggestions to clinical diagnoses to measure how often strokes are correctly identified.
Who could benefit from this research
Good fit: Ideal candidates are English-speaking adults who present to a mobile stroke unit, emergency department, or telestroke service with sudden speech changes, facial droop, or other acute neurological symptoms and who can speak during the brief test.
Not a fit: Patients who cannot speak or produce the required speech sample (for example, those who are intubated, non-English speakers, or severely impaired) or whose stroke signs are not reflected in facial/speech changes may not benefit from the app.
Why it matters
Potential benefit: If successful, the app could help clinicians find strokes sooner, reduce missed diagnoses, and speed patients to the right treatments.
How similar studies have performed: Some prior clinical tools and AI approaches have aided stroke recognition, but using combined facial-video and speech AI in bedside and telestroke triage is relatively new and not yet widely proven.
Where this research is happening
Houston, United States
- Methodist Hospital Research Institute — Houston, United States (Active)
Researchers
- Principal investigator: Wong, Stephen Tc — Methodist Hospital Research Institute
- Study coordinator: Wong, Stephen Tc
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 below.