BrainHemoAI: a deep-learning decision model to guide care for hemorrhagic stroke
Official title: Construction of an Integrated Intelligent Model for Spontaneous Intracerebral Hemorrhage Based on Deep Learning
Run by Second Affiliated Hospital of Nanchang University · NCT07570680 · Data updated 2026-11-12
This project will test whether the BrainHemoAI deep-learning model can help doctors use non-contrast CT scans to guide care for people aged 8 and older with spontaneous hemorrhagic stroke.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 8 Years and up |
| Sex | All |
| Enrollment | 7100 (estimated) |
| Sponsor | Second Affiliated Hospital of Nanchang University Academic / other |
| Locations | 1 site (Nanchang) |
| Status | Recruiting |
| Started | September 1, 2022 |
| Main results expected | August 1, 2026 |
| Study ends | December 30, 2026 |
| Registry record updated | May 6, 2026 |
| Trial ID | NCT07570680 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are people aged 8 or older with spontaneous hemorrhagic stroke who received non-contrast CT in the outpatient or emergency setting, were treated according to standard clinical guidelines, and have complete clinical data available.
✕Probably not a fit
Patients who had prior surgical treatment at another hospital, were in shock on admission, had severe life‑threatening organ dysfunction, died during hospitalization, or cannot complete follow-up are unlikely to benefit from this project.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational project will develop and apply a deep-learning decision model using clinical data and non-contrast CT images from patients with spontaneous hemorrhagic stroke treated at participating hospitals. The approach aims to move beyond the traditional Tada formula by combining imaging features and routine clinical variables to reduce subjective differences between clinicians. Data are collected retrospectively and prospectively under standard clinical care, excluding patients who had prior surgery elsewhere or who are not expected to complete follow-up. Collaborating centers include regional hospitals and a university partner to build a larger, more consistent dataset for model training and validation.
Why it matters
Potential benefit: If successful, the model could improve the accuracy and consistency of preoperative CT-based decision-making, potentially reducing death and disability and improving recovery after hemorrhagic stroke.
How similar studies have gone: Previous AI work has shown promise for detecting and measuring intracranial hemorrhage on CT, but using deep-learning decision models to standardize surgical or preoperative decision-making remains relatively novel and not yet widely proven.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Age \>= 8 years old; 2. Patients diagnosed with spontaneous hemorrhagic stroke based on medical history and auxiliary examinations; 3. Received non-contrast computed tomography (NCCT) in the outpatient or emergency department; 4. Treated in accordance with standard clinical guidelines during hospitalization; 5. Have complete clinical data. Exclusion Criteria: 1. Had undergone surgical treatment in another hospital before admission; 2. Was in a state of shock upon admission; 3. Had severe heart, liver, or kidney dysfunction or other life-threatening systemic diseases; 4. Died during hospitalization; 5. Had an expected lifespan of less than six months or was unable to complete the study follow-up for other reasons.
Where it's running
-
The Second Affiliated Hospital of Nanchang University
Nanchang, China Recruiting
Study contacts
- Study coordinator: Xingen Zhu, Prof
- Email: zxg2008vip@163.com
- Phone: 13803546020
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.