MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning

• URL: http://arxiv.org/abs/2410.03585v1
• Date: Fri, 4 Oct 2024 16:43:53 GMT
• Title: MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning
• Authors: Hassan Sartaj, Shaukat Ali, Julie Marie Gjøby,
• Abstract summary: We propose a meta-learning-based approach to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in OsloCity's context using five widely-used medical devices integrated with a real-world healthcare IoT application.
• Score: 3.229371159969159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices of various types. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants, and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City's health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach (MeDeT) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in OsloCity's context using five widely-used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT's ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.

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