UK CEiRSI Airlock Panel: Virtual Populations for TAVI Device Regulation

Описание: UK CEiRSI In Silico Regulatory Airlock Panel Session:
Establishing Credibility Recommendations for Generative Virtual Populations: Case Study in TAVI Device Regulation

About UK CEiRSI:
The UK’s first Centre of Excellence in In-Silico Regulatory Science and Innovation (UK CEiRSI) advances computational evidence in healthcare, bringing together academia, industry, and regulators to shape global standards.

About the In Silico Regulatory Airlock Panel Sessions:
These sessions bring together individuals from academia, industry, and regulatory bodies, to examine the practical strengths and weaknesses of existing international credibility frameworks. Each Case Study Panel Session will focus on a hypothetical medical intervention (drug or device) that will enable us to interrogate and refine current standards and frameworks for the credibility assessment of model-informed evidence.

About this Case Study:
Aim:
To establish credibility recommendations for AI-driven generative models in TAVI device regulation, addressing safety and efficacy uncertainties for younger patients by assessing whether the risk of permanent pacemaker implantation and moderate or greater paravalvular leakage in patients under 75 is equivalent to that in the elderly population with proven clinical trial outcomes.

Agenda:
Rationale for the use of conditional generative virtual populations of heart anatomy for targeted in-silico trials
Panel credibility recommendations for use of AI-driven generative models for TAVI safety and efficacy assessment
Panel and audience Q&A

Case Study Leads:
Dr David Flynn, Boston Scientific
Prof Alex Frangi, University of Manchester
Dr Arezoo Zakeri, University of Manchester

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