A Vision-Language Foundation Model for Brain Disease Diagnosis From Multimodal Data

NCT07126821 | Recruiting DMC

• 1 other identifier: 2025030464
• Study Type: observational
• Target: 100,000
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to develop an innovative, comprehensive, and explainable AI vision-language foundation model (VLM) to advance the diagnosis and interpretation of brain diseases using multi-modal data. We will include patient demographics, medical imaging data (such as MRI, CT, and PET scans), histopathological data, genomic data when available, and other necessary laboratory examinations and tests to establish a screening and diagnostic model for brain diseases.

Conditions

Brain (Nervous System) Cancers; Brain Arterial Disease; Neuro-Degenerative Disease; Brain Tumors; Brain Diseases; Neurological di

Keywords

brain tumors; brain cancers; brain diseases; foundation model; diagnosis; prediction; neurological diseases

Outcome Measures

Primary Outcomes (1)

• Brain Disease Diagnostic performance

  This study will evaluate how accurately the AI model can identify and differentiate between: 1. Brain tumors including gliomas, glioneuronal tumors, and neuronal tumor, meningioma, germ cell tumors, embryonal tumors, tumors of the sellar region, pineal region tumors, mesenchymal, non-meningothelial tumors, choroid plexus tumors, hematolymphoid tumors, cranial and paraspinal nerve tumors, melanocytic tumors and brain metastases based on WHO CNS 5 classification; 2. Brain diseases apart from brain tumors such as brain arterial disease, neurodegenerative disorders, etc.; 3. Normal brain findings; The model's performance will be assessed using sensitivity, specificity, F1-score AUC-ROC. Diagnostic ability of AI model will be compared against with pathological diagnosis(if possible), final clinical diagnoses by neurologists or radiologists.

  Perioperative

Interventions

No Interventions OTHER

No Interventions

Eligibility Criteria

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

Study Population

Patients with brain disease or Non-disease condition

You may qualify if:

• Patients with brain diseases:
• Patients with brain tumors were pathologically diagnosed.
• Patients with other brain diseases were correctly diagnosed.
• The clinical case data of all patients were complete.
• Non-brain disease population:
• All patients have complete clinical case data, complete brain MRI, no history brain diseases, no brain surgery or other brain diseases that affect the diagnosis and observation of MR imaging.

You may not qualify if:

• Cases in which MRI were incomplete or with significant noise and artifacts.

Sponsors & Collaborators

• Xiangya Hospital of Central South University: lead

Study Sites (1)

Xiangya Hospital of Central South University

Changsha, Hunan, 410008, China

RECRUITING

MeSH Terms

Conditions

Neurologic Manifestations; Neoplasms; Intracranial Arterial Diseases; Brain Neoplasms; Brain Diseases; Disease

Condition Hierarchy (Ancestors)

Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Cerebrovascular Disorders; Central Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Central Nervous System Neoplasms; Nervous System Neoplasms; Neoplasms by Site; Pathologic Processes

Central Study Contacts

Xuan Gong, PhD.

CONTACT

0086-731-8975-3037; gong.xuan@csu.edu.cn

Zhou Chen, PhD.

CONTACT

0086-13687397913; czad0412@163.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Consultant

Study Record Dates

• First Submitted: July 31, 2025
• First Posted: August 17, 2025
• Study Start: May 15, 2025
• Primary Completion (Estimated): December 31, 2030
• Study Completion (Estimated): December 31, 2030
• Last Updated: August 17, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)