Hemorrhage Stroke Decision Making Model Based Deep Learning (BrainHemoAI System)

NCT07570680 | Recruiting

• 1 other identifier: IIT-O-2025-054
• Study Type: observational
• Target: 7,100
• Locations: 1 country
• Sites: 1

Brief 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. 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

Outcome Measures

Primary Outcomes (1)

• Area Under Curve

  90-day and 180-day mRS score, survival status, functional independence (Barthel index).

  90-day and 180-day

Secondary Outcomes (1)

• Sensitivity ,Specificity,True Positive Rate,False Positive Rate

  Baseline (admission), 24 hours postoperatively, 3 days postoperatively, 7 days postoperatively, discharge, 90-day follow-up, 180-day follow-up

Interventions

Large language model DIAGNOSTIC_TEST

Automatical diagnosis, treatment decision-making, and risk prediction after spontaneous intracerebral hemorrhage via a trained deep learning larger language model.

Also known as: deep learning based diagnosis and treatment decision-making model

Eligibility Criteria

• Age: 8 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Spontaneous hemorrhagic stroke, including parenchymal hemorrhage, subarachnoid hemorrhage, intraventricular hemorrhage, and subdural hemorrhage(including chronic subdural hemorrhage).

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Second Affiliated Hospital of Nanchang University: lead
• Zhejiang University: collaborator
• Ganzhou City People's Hospital: collaborator
• The First People's Hospital of Xiushui: collaborator
• Jiujiang No.1 People's Hospital: collaborator
• Renmin Hospital of Wuhan University: collaborator
• First Affiliated Hospital of Gannan Medical University: collaborator
• First Affiliated Hospital of Zhejiang University: collaborator

Study Sites (1)

The Second Affiliated Hospital of Nanchang University

Nanchang, China

RECRUITING

Related Publications (4)

• 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.

  PMID: 40071129 RESULT

• Hu 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.

  PMID: 38552240 RESULT

• Hu 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.

  PMID: 38489547 RESULT

• Hu 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.

  PMID: 37574119 RESULT

MeSH Terms

Interventions

Large Language Models

Intervention Hierarchy (Ancestors)

Deep Learning; Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts; Neural Networks, Computer

Central Study Contacts

Xingen Zhu, Prof

CONTACT

13803546020; zxg2008vip@163.com

Ping Hu, PhD;MD

CONTACT

13207109734; hp666edu@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, Chief Physician

Study Record Dates

• First Submitted: April 29, 2026
• First Posted: May 6, 2026
• Study Start: September 1, 2022
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): December 30, 2026
• Last Updated: May 6, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)