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

August 15, 2025 updated by: Xuan Gong, Xiangya Hospital of Central South University
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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Secondary Objective: To establish a comprehensive diagnostic model with uncertainty quantification and automated report generation that covers all brain diseases based on clinical indicators.

Exploratory Objective: To include MRI scans from large-scale populations for model validation.

Study Type

Observational

Enrollment (Estimated)

100000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • China
    • Hunan
      • Changsha, Hunan, China, 410008
        • Recruiting
        • Xiangya Hospital of Central South University
        • Contact:
        • Contact:
        • Principal Investigator:
          • Xuan Gong, Ph.D

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Patients with brain disease or Non-disease condition

Description

Inclusion Criteria:

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.

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Brain Disease Diagnostic performance
Time Frame: Perioperative
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Xiangya Hospital of Central South University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 15, 2025

Primary Completion (Estimated)

December 31, 2030

Study Completion (Estimated)

December 31, 2030

Study Registration Dates

First Submitted

July 31, 2025

First Submitted That Met QC Criteria

August 15, 2025

First Posted (Actual)

August 17, 2025

Study Record Updates

Last Update Posted (Actual)

August 17, 2025

Last Update Submitted That Met QC Criteria

August 15, 2025

Last Verified

August 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2025030464

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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