Trustworthy TAVR navigator system, I: A generative adversarial network-driven medical twin approach for Post-TAVR pacemaker implantation prediction

Lilia Tightiz, Min Hyck Jung, InSeo Song, KangYoon Lee

Abstract: This paper explores the advanced integration of predictive models in transcatheter aortic valve replacement (TAVR) to improve post-procedural outcomes and address the risk of conduction abnormalities (CA), which frequently require permanent pacemaker implantation (PPI). We investigate critical considerations, from enhancing model reliability and interactivity for physicians to ensuring data confidentiality, advancing the development of intelligent healthcare solutions, and bridging the gap between technological innovation and practical medical application. To overcome data scarcity, we use a penalized gradient policy over the conditional generative adversarial networks (CGAN) as a solution for data augmentation. Furthermore, we present a unique tool to doctors for simulating and assessing probable surgical consequences: a digital twin framework for visualizing the requirement of pacemaker implantation post-TAVR. We aim to simplify complex predictions for healthcare practitioners by combining Explainable AI (XAI) with medical twins. Additionally, we have set up a roadmap for the TAVR navigator system that adheres to stringent ethical and legal criteria while respecting reliable AI features like validity, confidentiality, responsiveness, and privacy. It will guarantee that the development and implementation of our system focuses on ethics and transparency.