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TEPSIM

Transarterial Embolization Planning Simulator
Eco healthcare
Spoke5_TEPSIMimage
Scheda del progetto
Data
Start date
Valore
Total value
€ 449.823,00
Durata
Duration
18 months
Investimento
Investment nodes
€ 292.190,91

The TEPSIM project focuses on optimizing the transarterial embolization (TAE) procedure for the treatment of liver cancer that involves blocking the blood supply to the tumor. The approach aims to effectively plan the embolization procedures using specific 3D virtual models tailored to the patient.

The personalized medicine software platform developed by Medics will allow extraction and visualization of 3D models of the liver and intrahepatic vascular structures from medical imaging data. The OdR will contribute as a specialist consultant for creating personalized models of transport phenomena in the hepatic system based on hemodynamic parameters measured in vivo. 

 

Contact:

Giuseppe Isu

giuseppe.isu@medics3d.com 

 

The goal
Document

Objectives include the development and validation of a machine learning algorithm for 3D models, intrarterial embolization simulation software, and a specific digital twin platform for TAE simulation/planning. The project aims to enhance the accuracy and effectiveness of TAE, significantly contributing to clinical practice in liver cancer treatment.  

Why is it innovative
Document

Medics' project aims to revolutionize clinical treatment planning by integrating robust functionalities into a platform based on their know-how and AI capabilities. Their established experience and international distribution anticipate a significant impact even beyond national borders. With an expected TRL of 6, their ambition is supported by a strong technological and clinical foundation. 

Impact on the users
Document

TEPSIM project aims to transform medicine through the use of AI-enhanced personalized predictive models. These models, integrating multi-modal patient data, will enable personalized prediction regarding disease risk and treatment response.However, there are challenges such as managing algorithm learning and standardizing data. The project also promotes economic and employment growth with the potential to make advanced diagnostic tools accessible and improve patient outcomes.