Digital Twins for Biofluids

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Imaging

Background:

• Digital twins offer dynamic virtual representations of physical systems.
• In biofluids, they integrate physics-based models with clinical data for diagnostics, therapy planning, and device design.

Purpose of the Study:

• To review modeling approaches for constructing digital twins in biofluid applications.
• To highlight the strengths and limitations of various numerical and machine learning techniques.
• To discuss key requirements for digital twin development, including bidirectional interaction and context-specific modeling.

Main Methods:

• Survey of high-fidelity numerical methods.
• Exploration of emerging machine learning techniques.
• Discussion of essential digital twin requirements and modeling strategy selection.

Main Results:

• Digital twins enable real-time prediction, optimization, and personalization in healthcare.
• Integration of physics-based and data-driven methods is crucial for biofluid applications.
• Progress has been made, but challenges in multiphysics integration and standardization persist.

Conclusions:

• Digital twins hold significant potential for transforming biomedical engineering and healthcare.
• Further research is needed to overcome challenges in integrating diverse modeling approaches and establishing data standards.
• Tailored modeling strategies are essential for successful digital twin implementation in specific biomedical contexts.