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Related Experiment Video

Updated: Feb 27, 2026

Taking Advantage of Reduced Droplet-surface Interaction to Optimize Transport of Bioanalytes in Digital Microfluidics

Taking Advantage of Reduced Droplet-surface Interaction to Optimize Transport of Bioanalytes in Digital Microfluidics

Published on: November 10, 2014

Digital Twins for Biofluids.

C Alberto Figueroa^1,2, Krishna Garikipati³, Haizhou Yang¹

¹Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA;

Annual Review of Biomedical Engineering

|February 25, 2026

Summary

This summary is machine-generated.

Digital twins, virtual models linked to physical systems, can revolutionize biomedical engineering for health and disease. This review explores modeling for biofluid digital twins, integrating physics and data for diagnostics and device design.

Keywords:

biofluids digital twins machine learning multiphysics multiscale modeling reduced-order model

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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.