透视图(图形)
组织工程
医学
生物医学工程
计算机科学
人工智能
作者
Sara Sebastiani,Federica Buccino,Zhao Qin,L. Vergani
出处
期刊:Matter
[Elsevier BV]
日期:2025-09-01
卷期号:8 (9): 102252-102252
被引量:13
标识
DOI:10.1016/j.matt.2025.102252
摘要
Bone tissue engineering (BTE) presents a transformative solution for critical-sized bone defects, yet optimizing scaffold geometry remains a significant challenge. Inspired by the natural structure of bone, this work explores five pivotal geometrical parameters—porosity, pore size, pore architecture, interconnectivity and permeability, and curvature—and elucidates their impact on scaffold performance. Approximately 70% porosity, mid-sized pores (∼400–650 μm), high interconnectivity, and concave surfaces emerge as the most promising features for bone regeneration, while optimal pore architecture remains cryptic. In the intricate design space defined by the interdependence of these parameters, artificial intelligence (AI) is proposed as a tool to accelerate the scaffold design process. By critically evaluating the implications of scaffold geometry, this work sheds light on current research gaps and lays a strong foundation for future studies. Integrating experimental findings with AI-driven insights, it paves the way for the design of more effective and clinically applicable BTE scaffolds.
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