New research explores cardiovascular digital twins and calibration methods · 2 sources tracked

Two new arXiv papers explore the development and calibration of digital twins for cardiovascular health. The first paper reviews various modeling approaches, from physics-based to data-driven, highlighting the integration of physical constraints with relational learning. The second paper focuses on calibration issues in cardiac digital twins, proposing a Convention-Aware EF Audit protocol to distinguish genuine measurement calibration from artifacts, using echocardiographic data and models like EchoNet-Dynamic. AI

IMPACT These papers contribute to advancing AI-driven medical modeling and diagnostics, potentially improving patient care through more accurate and robust digital twin simulations.

RANK_REASON Two academic papers published on arXiv discussing novel approaches and calibration methods for cardiovascular digital twins.

Read on arXiv cs.CV →

• alphaXiv
• arXiv
• Camus
• Cardiovascular digital twins
• CatalyzeX
• CORE Recommender
• DagsHub
• EchoNet-Dynamic
• GitHub
• Gotit.pub
• Graph Based Representations in Pattern Recognition
• Hugging Face
• Influence Flower
• physics-informed neural networks
• ScienceCast
• statistical relational learning

• paper
• other

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