INSILICO

Hybrid Machine Learning for Virtual Chimaeras and In-Silico Trials — Cardiovascular Device Innovation & Regulatory Science

Funder: European Research Council Advanced Grant / UK Research and Innovation Frontier Research Guarantee
Period: 2023 – 2028
Principal Investigator: Prof. Alejandro F. Frangi

overview

INSILICO — Hybrid Machine Learning for Virtual Chimaeras and In-Silico Trials will establish the first integrated framework combining data- and knowledge-driven machine learning, realising in-silico trials (ISTs) in medical devices (MDs). Novel in-silico insights on MD safety and efficacy will significantly impact regulatory science and innovation by reducing R&D costs and speeding up regulatory clearance.

objectives

The project will:

  1. Introduce the concept of virtual chimaeras.
  2. Extend physics-informed learning over graph networks to construct new reliable, accurate, and fast multiphysics simulators.
  3. Re-enact a unique industry-provided trial dataset to grow trust in ISTs by industry, trialists, and regulators.

key impact

INSILICO will deliver the first head-to-head comparison between a Randomised Controlled Trial and an In Silico Trial, using a real-world dataset from an $80m trial on cardiac prosthetic valves for heart failure.