Computational Advances in Biomaterial Engineering: Mapping Research Trajectories Through Bibliometric Analysis.

Area of Science:

• Biomaterials Science
• Computational Biology
• Tissue Engineering

Background:

• Computational models are increasingly prioritized in biomedical research as alternatives to animal testing.
• Despite policy shifts, computational models are underutilized in tissue engineering and regenerative medicine R&D.
• This study provides a bibliometric analysis of computational techniques in regenerative biomaterials.

Purpose of the Study:

• To identify current and emerging computational techniques in regenerative biomaterials.
• To examine future directions in tissue engineering computational modeling.
• To inform researchers on applying computational methods for biomaterial solutions.

Main Methods:

• Bibliometric analysis of 678 studies from Web of Science (Jan 2014–Mar 2025).
• Studies grouped by computational method (e.g., computational fluid dynamics [CFD], molecular dynamics [MD]) and tissue type.
• Co-citation and co-keyword network analyses were employed.

Main Results:

• Computational fluid dynamics/finite element modeling (CFD/FEM) is the most common method, optimizing material properties like viscoelasticity and porosity.
• Parameter estimation and sensitivity analysis are key applications across methods.
• Modeling of stem cell biomaterials is emerging, with hybrid and data-driven models (AI/ML) gaining traction.

Conclusions:

• Hybrid models integrating CFD/FEM with MD are expected to increase.
• Data-driven models (AI/ML) show promise but require addressing data scarcity and interpretability for regulatory standards.
• Integrating data-driven and mechanistic models offers synergistic solutions for biomaterial innovation in tissue engineering.