Imagine trying to decide if a bridge needs reinforcing just by looking at a photo, without knowing how much weight it actually carries. Currently, doctors do this with spines affected by cancer, often guessing if a patient needs surgery. This project builds a smart digital simulator that tests the spine's strength to tell doctors exactly who is at risk of a break. It turns a subjective guess into a precise, data-backed decision.
Clinicians currently rely on subjective radiographic scoring to decide on spinal surgery for cancer patients, leaving 60% of cases with uncertain diagnoses. This leads to either unnecessary surgeries or preventable fractures that degrade patient quality of life.
A Decision Support System (DSS) integrating AI and biomechanical models, a segmentation toolbox, and new clinical guidelines for patient stratification.
If you are a software developer dealing with imprecise diagnostic tools — this project developed a Decision Support System (DSS) that reduces uncertain diagnoses from 60% down to 20%. This allows for the creation of a high-precision clinical tool for spinal care.
If you are an implant provider dealing with inefficient surgery scheduling — this project developed biomechanically validated models to identify the best personalized surgical treatment. This ensures implants are used only for the 30% of patients who actually face fracture risks.
If you are a hospital manager dealing with high costs of unnecessary surgeries — this project developed a stratification strategy that could cut expenditure by 2.4B€/year. This optimizes resource allocation and improves patient quality of life.
Based on available project data, the approach is expected to allow cutting expenditure by 2.4B€/year.
The models and the Decision Support System (DSS) are specifically designed to be suitable for regulatory requirements and future exploitation.
Based on available project data, the project focuses on creating a toolbox and DSS for clinical settings, though specific licensing terms are not detailed.
The system is trained on 2000 retrospective cases and 120 ex vivo specimens, then tested in a multicentric prospective study with 200 patients.
The project runs from July 2023 to June 2028, indicating a multi-year development and validation cycle.
The consortium is well-balanced for commercialization, featuring 16 partners across 9 countries. With a 44% industry ratio (7 companies, 6 of which are SMEs), there is a strong bridge between academic research (8 universities) and market application, ensuring the resulting DSS is designed for regulatory compliance.
Contact ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNA
Contact us to explore licensing opportunities for the METASTRA Decision Support System.
