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

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.

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.

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.