Thermomechanical dynamics and statistical optimization in bone drilling: A systematic review on mitigating thermal necrosis through multiscale modeling and clinical validation

This systematic review advances the field of bone drilling by synthesizing thermomechanical dynamics, computational modeling, and clinical validation into a cohesive framework for minimizing thermal necrosis and mechanical trauma. Diverging from prior studies that isolate thermal or mechanical factors, this work quantifies nonlinear interactions among drill geometry (point angle, helix angle, diamond coatings), operational parameters (feed rate: 10–50 $\frac{mm}{min}$, rotational speed: 500–2500 RPM), and bone heterogeneity (cortical bone) through advanced sensitivity analyses (Sobol indices, E-FAST) and multiscale predictive models (±5 % accuracy). By synthesizing data from 152 studies, this review proposes a patient-specific protocol matrix validated across diverse bone types. Key findings include the identification of optimal thresholds and hybrid cooling strategies, which collectively reduce thermal necrosis risk by 22–30 %. While AI-driven multi-objective optimization is briefly explored for dynamic parameter adaptation, the core focus lies in statistical methodologies and multiscale simulations (FEM/CFD) to enhance predictive precision. The study underscores the need for standardized surgical protocols and provides a roadmap for improving clinical outcomes through robust thermomechanical control.