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Digital Twin and Ml-basEd MOdel of TEVAR Interventions
Sponsor: Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico
Summary
The study aims to collect clinical data and pseudonymized CT images of patients undergoing TEVAR in order to create an anatomical digital twin capable of simulating procedural outcomes and training machine learning (ML) algorithms. This approach will support predictive models that may assist physicians in selecting the optimal medical device, improving pre-TEVAR planning, and predicting post-TEVAR complications.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
5000
Start Date
2026-02-11
Completion Date
2026-09-30
Last Updated
2026-06-11
Healthy Volunteers
No
Conditions
Locations (1)
Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico
Milan, Italy