•  
  •  
 

ELECTROMAGNETIC CONTROL AND AI-DRIVEN OPTIMIZATION OF SCAFFOLD-MEDIATED TISSUE REGENERATION**

Abstract

Scaffold-mediated tissue regeneration is limited not only by biological factors but by the lack of precise, controllable stimulation to drive growth toward healthy tissue density. This work develops a reduced-order, control-oriented model of a Fe₃O₄ nanoparticle–loaded scaffold under electromagnetic stimulation and uses AI-based optimization to tune stimulation protocols. Cell density is modeled as a normalized fraction of healthy tissue (N=1 representing pre-injury density) and follows a logistic-type growth law with environmental crowding. EM stimulation influences regeneration through two coupled pathways. First, drug release from the scaffold is described by a Weibull function whose effective time constant decreases in a saturating manner with EM intensity, accelerating growth-factor availability. Second, both EM intensity and drug concentration modulate the effective proliferation rate via Hill-type saturating functions, while an additional EM-dependent damage term captures inhibitory effects at high field strengths. The resulting system is treated as a controllable plant with EM intensity as the primary input and normalized tissue recovery as the output. A Bayesian optimization framework is employed to calibrate model parameters against literature data and to identify EM regimes that enhance recovery toward healthy tissue levels within safety constraints. Simulations characterize input–output behavior and demonstrate how electrical engineering and AI tools can transform passive regenerative scaffolds into actively controlled therapeutic platforms.

Acknowledgements

VSU Dept. of Engineering technology and Computer science

This document is currently not available here.

Share

COinS