Hemorrhage StrokeNCT07570680
Hemorrhage Stroke Decision Making Model Based Deep Learning (BrainHemoAI System)
An observational study of Hemorrhage Stroke, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
Trial summary
Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke in China. Read more about this study
- Who is it for?
- Ages 8 years and olderHealthy volunteers can take part
- Conditions the study is for
- Hemorrhage Stroke
- What is being tested?
- Large language model
- How many people
- 7,100 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2022to 30 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 8 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Spontaneous hemorrhagic stroke, including parenchymal hemorrhage, subarachnoid hemorrhage, intraventricular hemorrhage, and subdural hemorrhage(including chronic subdural hemorrhage).
Who may be able to join
- Age >= 8 years old;
- Patients diagnosed with spontaneous hemorrhagic stroke based on medical history and auxiliary examinations;
- Received non-contrast computed tomography (NCCT) in the outpatient or emergency department;
- Treated in accordance with standard clinical guidelines during hospitalization;
- Have complete clinical data.
Who cannot join
- Had undergone surgical treatment in another hospital before admission;
- Was in a state of shock upon admission;
- Had severe heart, liver, or kidney dysfunction or other life-threatening systemic diseases;
- Died during hospitalization;
- Had an expected lifespan of less than six months or was unable to complete the study follow-up for other reasons.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Second Affiliated Hospital of Nanchang University
Nanchang
Xingen Zhu, 13803546020, zxg2008vip@163.com
About this study
Official title: Construction of an Integrated Intelligent Model for Spontaneous Intracerebral Hemorrhage Based on Deep Learning
Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke in China. The current preoperative imaging evaluation of spontaneous cerebral hemorrhage is still limited to the traditional Tada formula, and there are subjective differences in diagnosis among different doctors, making it difficult to achieve homogenization in clinical decision-making. Hemorrhagic stroke is a common and frequently occurring disease in Jiangxi Province. Therefore, establishing a new diagnosis and treatment system focused on hemorrhagic stroke can not only fill the research gap in this field in China, improve the accuracy and homogeneity of hemorrhagic stroke diagnosis and treatment, but also promote related research progress to reduce the mortality and disability rates of this disease and improve the clinical prognosis of patients.
Read the detailed description
Hemorrhagic stroke is a serious cerebrovascular disease, accounting for about 20% of all strokes. It refers to cerebral hemorrhage and subarachnoid hemorrhage caused by intracranial vascular diseases such as intracranial aneurysms, cerebral and spinal vascular malformations and moyamoya disease under the effect of blood flow. It has the characteristics of high incidence rate, high disability rate and high mortality rate, and has caused huge economic burden to patients, families and society.
Hemorrhagic stroke is one of the high-risk diseases in Jiangxi Province, and has become a major public health challenge and a key social issue that urgently needs to be addressed. On the one hand, the diagnosis and treatment of hemorrhagic stroke have a certain degree of complexity, involving multiple disciplines, especially neurology and endocrinology, which have established multiple diagnostic, evaluation, treatment, and rehabilitation systems. Different systems have different focuses, but limited by the level of understanding of the disease, there have been only basic treatment principles for decades, and there has been no breakthrough in specific treatment plans. On the other hand, with the development of the economy and the improvement of living standards, clinical physicians and patients not only focus on the survival rate after hemorrhagic stroke, but also pay more attention to neurological function recovery and long-term quality of life. Due to the limitations of detection technology in the past, it was difficult to accurately describe diseases and truly develop individualized diagnosis and treatment plans, resulting in significant differences in patient prognosis. How to leverage advances in diagnosis and treatment technology to ultimately achieve precision, individualization, and homogenization in the diagnosis and treatment of hemorrhagic stroke is a key focus for the future.
Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke in China. The current preoperative imaging evaluation of spontaneous cerebral hemorrhage is still limited to the traditional Tada formula, and there are subjective differences in diagnosis among different doctors, making it difficult to achieve homogenization in clinical decision-making. Hemorrhagic stroke is a common and frequently occurring disease in Jiangxi Province. Therefore, establishing a new diagnosis and treatment system focused on hemorrhagic stroke can not only fill the research gap in this field in China, improve the accuracy and homogeneity of hemorrhagic stroke diagnosis and treatment, but also promote related research progress to reduce the mortality and disability rates of this disease and improve the clinical prognosis of patients.
- Conditions
- Hemorrhage Stroke
- Being tested
- Large language model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large language modelDiagnostic test
Also called deep learning based diagnosis and treatment decision-making model
Automatical diagnosis, treatment decision-making, and risk prediction after spontaneous intracerebral hemorrhage via a trained deep learning larger language model.
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under Curve
90-day and 180-day mRS score, survival status, functional independence (Barthel index).
Measured: 90-day and 180-day
Secondary outcomes (1)
Sensitivity ,Specificity,True Positive Rate,False Positive Rate
Including hospital stay, ICU stay, hospitalization costs, rebleeding, delayed cerebral ischemia, intracranial infection, and hydrocephalus flow surgery needs
Measured: Baseline (admission), 24 hours postoperatively, 3 days postoperatively, 7 days postoperatively, discharge, 90-day follow-up, 180-day follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 May 2026
- Study start1 Sep 2022
- Primary completion1 Aug 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Hemorrhage StrokeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 7,100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital of Nanchang University
- Sponsor type
- University, hospital or other
- Collaborators
- First Affiliated Hospital of Gannan Medical University, First Affiliated Hospital of Zhejiang University, Ganzhou City People's Hospital, The First People's Hospital of Xiushui, Jiujiang No.1 People's Hospital, Renmin Hospital of Wuhan University
- Responsible party
- Principal investigator, Xingen Zhu, Second Affiliated Hospital of Nanchang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07570680
- Sponsor's protocol ID
- IIT-O-2025-054
- Status last verified
- Apr 2026
- First submitted
- 29 Apr 2026
- Last updated
- 6 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M. Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment. Ther Clin Risk Manag. 2025 Mar 7;21:293-307. doi: 10.2147/TCRM.S504745. eCollection 2025.
PubMed 40071129Hu P, Wu Y, Yan T, Shu L, Liu F, Xiao B, Ye M, Wu M, Lv S, Zhu X. Deep learning-based quantification of total bleeding volume and its association with complications, disability, and death in patients with aneurysmal subarachnoid hemorrhage. J Neurosurg. 2024 Mar 29;141(2):343-354. doi: 10.3171/2024.1.JNS232280. Print 2024 Aug 1.
PubMed 38552240Hu P, Yan T, Xiao B, Shu H, Sheng Y, Wu Y, Shu L, Lv S, Ye M, Gong Y, Wu M, Zhu X. Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis. Int J Surg. 2024 Jun 1;110(6):3839-3847. doi: 10.1097/JS9.0000000000001266.
PubMed 38489547Hu P, Zhou H, Yan T, Miu H, Xiao F, Zhu X, Shu L, Yang S, Jin R, Dou W, Ren B, Zhu L, Liu W, Zhang Y, Zeng K, Ye M, Lv S, Wu M, Deng G, Hu R, Zhan R, Chen Q, Zhang D, Zhu X. Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet. Neuroimage. 2023 Oct 1;279:120321. doi: 10.1016/j.neuroimage.2023.120321. Epub 2023 Aug 11.
PubMed 37574119
Study at a glance
From the registry record, last updated by the sponsor on 6 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction of an Integrated Intelligent Model for Spontaneous Intracerebral Hemorrhage Based on Deep Learning
- ClinicalTrials.gov ID
- NCT07570680
- Acronym / sponsor ID
- IIT-O-2025-054
- Conditions
- Hemorrhage Stroke
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 7,100 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital of Nanchang UniversityUniversity, hospital or other
- Collaborators
- First Affiliated Hospital of Gannan Medical University, First Affiliated Hospital of Zhejiang University, Ganzhou City People's Hospital, The First People's Hospital of Xiushui, Jiujiang No.1 People's Hospital, Renmin Hospital of Wuhan University
- Study start
- 1 Sep 2022 (actual)
- Primary completion
- 1 Aug 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 6 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke in China. The current preoperative imaging evaluation of spontaneous cerebral hemorrhage is still limited to the traditional Tada formula, and there are subjective differences in diagnosis among different doctors, making it difficult to achieve homogenization in clinical decision-making. Hemorrhagic stroke is a common and frequently occurring disease in Jiangxi Province. Therefore, establishing a new diagnosis and treatment system focused on hemorrhagic stroke can not only fill the research gap in this field in China, improve the accuracy and homogeneity of hemorrhagic stroke diagnosis and treatment, but also promote related research progress to reduce the mortality and disability rates of this disease and improve the clinical prognosis of patients.
Detailed description
Hemorrhagic stroke is a serious cerebrovascular disease, accounting for about 20% of all strokes. It refers to cerebral hemorrhage and subarachnoid hemorrhage caused by intracranial vascular diseases such as intracranial aneurysms, cerebral and spinal vascular malformations and moyamoya disease under the effect of blood flow. It has the characteristics of high incidence rate, high disability rate and high mortality rate, and has caused huge economic burden to patients, families and society.
Hemorrhagic stroke is one of the high-risk diseases in Jiangxi Province, and has become a major public health challenge and a key social issue that urgently needs to be addressed. On the one hand, the diagnosis and treatment of hemorrhagic stroke have a certain degree of complexity, involving multiple disciplines, especially neurology and endocrinology, which have established multiple diagnostic, evaluation, treatment, and rehabilitation systems. Different systems have different focuses, but limited by the level of understanding of the disease, there have been only basic treatment principles for decades, and there has been no breakthrough in specific treatment plans. On the other hand, with the development of the economy and the improvement of living standards, clinical physicians and patients not only focus on the survival rate after hemorrhagic stroke, but also pay more attention to neurological function recovery and long-term quality of life. Due to the limitations of detection technology in the past, it was difficult to accurately describe diseases and truly develop individualized diagnosis and treatment plans, resulting in significant differences in patient prognosis. How to leverage advances in diagnosis and treatment technology to ultimately achieve precision, individualization, and homogenization in the diagnosis and treatment of hemorrhagic stroke is a key focus for the future.
Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke in China. The current preoperative imaging evaluation of spontaneous cerebral hemorrhage is still limited to the traditional Tada formula, and there are subjective differences in diagnosis among different doctors, making it difficult to achieve homogenization in clinical decision-making. Hemorrhagic stroke is a common and frequently occurring disease in Jiangxi Province. Therefore, establishing a new diagnosis and treatment system focused on hemorrhagic stroke can not only fill the research gap in this field in China, improve the accuracy and homogeneity of hemorrhagic stroke diagnosis and treatment, but also promote related research progress to reduce the mortality and disability rates of this disease and improve the clinical prognosis of patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 8 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Spontaneous hemorrhagic stroke, including parenchymal hemorrhage, subarachnoid hemorrhage, intraventricular hemorrhage, and subdural hemorrhage(including chronic subdural hemorrhage).
Key inclusion criteria (5 listed)
- Age >= 8 years old;
- Patients diagnosed with spontaneous hemorrhagic stroke based on medical history and auxiliary examinations;
- Received non-contrast computed tomography (NCCT) in the outpatient or emergency department;
- Treated in accordance with standard clinical guidelines during hospitalization;
- Have complete clinical data.
Key exclusion criteria (5 listed)
- Had undergone surgical treatment in another hospital before admission;
- Was in a state of shock upon admission;
- Had severe heart, liver, or kidney dysfunction or other life-threatening systemic diseases;
- Died during hospitalization;
- Had an expected lifespan of less than six months or was unable to complete the study follow-up for other reasons.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large language modelAlso: deep learning based diagnosis and treatment decision-making modelAutomatical diagnosis, treatment decision-making, and risk prediction after spontaneous intracerebral hemorrhage via a trained deep learning larger language model. | Diagnostic test | – |
Primary outcome
Area Under Curve
Time frame: 90-day and 180-day
90-day and 180-day mRS score, survival status, functional independence (Barthel index).
1 secondary outcome
Sensitivity ,Specificity,True Positive Rate,False Positive Rate
Time frame: Baseline (admission), 24 hours postoperatively, 3 days postoperatively, 7 days postoperatively, discharge, 90-day follow-up, 180-day follow-up
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 May 2026.
- Results publication: PubMed 40071129Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M. Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment. Ther Clin Risk Manag. 2025 Mar 7;21:293-307. doi: 10.2147/TCRM.S504745. eCollection 2025.
- Results publication: PubMed 38552240Hu P, Wu Y, Yan T, Shu L, Liu F, Xiao B, Ye M, Wu M, Lv S, Zhu X. Deep learning-based quantification of total bleeding volume and its association with complications, disability, and death in patients with aneurysmal subarachnoid hemorrhage. J Neurosurg. 2024 Mar 29;141(2):343-354. doi: 10.3171/2024.1.JNS232280. Print 2024 Aug 1.
- Results publication: PubMed 38489547Hu P, Yan T, Xiao B, Shu H, Sheng Y, Wu Y, Shu L, Lv S, Ye M, Gong Y, Wu M, Zhu X. Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis. Int J Surg. 2024 Jun 1;110(6):3839-3847. doi: 10.1097/JS9.0000000000001266.
- Results publication: PubMed 37574119Hu P, Zhou H, Yan T, Miu H, Xiao F, Zhu X, Shu L, Yang S, Jin R, Dou W, Ren B, Zhu L, Liu W, Zhang Y, Zeng K, Ye M, Lv S, Wu M, Deng G, Hu R, Zhan R, Chen Q, Zhang D, Zhu X. Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet. Neuroimage. 2023 Oct 1;279:120321. doi: 10.1016/j.neuroimage.2023.120321. Epub 2023 Aug 11.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hemorrhage Stroke, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xingen Zhu, Prof (13803546020, zxg2008vip@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under Curve (time frame: 90-day and 180-day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07570680, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 May 2026.