Automated Multimodal Data Integration and Analysis for Pediatric Brain Tumor Imaging and Pathology

This project aims to develop an advanced, automated system to improve the diagnosis and treatment of pediatric brain tumors. By integrating various types of medical data, including MRI scans, digital pathology slides, and clinical reports, we hope to create a comprehensive tool that can assist clinicians and researchers in more effectively identifying and understanding these complex diseases.
Our approach uses cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to analyze medical images and extract relevant features. These features will be combined with textual data from pathology, radiology, and surgical reports to provide a holistic view of each patient’s condition. The goal is to enhance the accuracy and speed of diagnosis, predict disease progression, and tailor treatment plans to individual patients.
By leveraging data from the Children’s Brain Tumor Network (CBTN), which includes a rich collection of pediatric brain tumor cases, we aim to validate our system and demonstrate its potential in a real-world clinical setting. This project will culminate in a proof-of-concept demonstration by October, showcasing the system’s capabilities and laying the groundwork for future enhancements and broader applications.