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NCT Number: NCT07126821

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

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.

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Key information

Conditions

Brain (Nervous System) Cancers Brain Arterial Disease Brain Diseases Brain Neoplasms Brain Tumors Cardiovascular Diseases Central Nervous System Diseases Central Nervous System Neoplasms Cerebrovascular Disorders Disease Intracranial Arterial Diseases Neoplasms Neoplasms by Site Nervous System Diseases Nervous System Neoplasms Neuro-Degenerative Disease Neurologic Manifestations Neurological di Pathologic Processes Pathological Conditions, Signs and Symptoms Signs and Symptoms Vascular Diseases

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

No interventions

Other

No Interventions

Primary outcomes

  1. 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Xuan Gong, PhD.

CONTACT

[email protected]

0086-731-8975-3037

Zhou Chen, PhD.

CONTACT

[email protected]

0086-13687397913

Sponsors and collaborators

Lead sponsor

Xiangya Hospital of Central South University

Other

Registry information

Important dates

Study start
2025
Primary completion
2030
Study completion
2030
First posted
Aug 17, 2025
Registry last updated
Aug 17, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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