Value Proposition
· Combines MRI imaging and clinical variables for a comprehensive and accurate prediction of brain tumor outcomes and can automates complex analyses to save time for clinicians while supporting personalized patient counseling and optimized treatment strategies.
· Demonstrates proven accuracy through validation on 600 glioblastoma patient dataset.
· Designed to scale beyond glioblastoma to all brain tumor types, significantly increasing utility and market potential.
Unmet Need
· Currently, there is a lack of accurate and comprehensive tools to predict survival and outcomes for brain tumor patients after surgery.
· The current gold standard relies on fragmented approaches, such as manual interpretation of MRI scans and individual clinical variables, which are time-intensive and prone to variability.
· These methods lack the precision and integration necessary for personalized patient counseling and treatment planning.
· Therefore, there is a strong need for a technology that combines MRI imaging and clinical data to deliver accurate, scalable, and actionable prognostic insights to address this critical gap in post-surgical care.
Technology Description
· Researchers at Johns Hopkins have developed a novel AI-driven technology that addresses the unmet need for accurate prognostication of brain tumor outcomes following surgery.
· This innovation integrates MRI imaging and clinical variables into a single predictive model, offering a comprehensive and precise assessment of patient survival and post-surgical outcomes.
· The technology leverages advanced machine learning algorithms trained on a dataset of 600 glioblastoma patients to provide actionable insights for clinicians.
· Preliminary data demonstrates high accuracy, validating its potential to significantly enhance personalized patient care and treatment planning.
Stage of Development
· Proof-of-concept stage
Data Availability
· N/A
Publication
· N/A
