Finite element analysis-assisted surgical planning and evaluation of flap design in hand surgery - Yang, Guang; Shen, Hui; Jang, Yewon; Cheng, Xiangyi (Frontiers in Bioengineering and Biotechnology, 2025)
Given the anatomical variability among patients and the intricate geometry of the hand, the shape and size of the skin flap have traditionally relied heavily on the surgeon’s experience and subjective judgment. This dependence can lead to inconsistent and sometimes suboptimal results, particularly in complex cases such as web reconstruction in syndactyly surgery. Finite element analysis (FEA) provides a quantitative method to simulate and optimize skin flap design during surgery. However, existing FEA studies in this field are scattered across a wide range of seemingly unrelated topics. To address this, we present a comprehensive review focused on the application of FEA in skin flap design since 2000, with attention to all aspects of preprocessing and postprocessing. The primary objective is to evaluate the potential of FEA to generate patient-specific models by integrating individualized anatomical and biomechanical data while identifying key advancements, analyzing methodological challenges, exploring emerging technologies, and outlining future research directions. A critical finding is that the mechanical modeling of skin remains a major limitation in current FEA applications. To address this, future studies should focus on the development and refinement of non-invasive techniques for acquiring patient-specific skin properties. We also recommend several additional research directions based on our findings. These include exploring techniques to unfold 3D wound surfaces into 2D representations, which can improve mesh quality and computational efficiency; validating FEA simulations through large-scale, multicenter clinical studies to ensure robustness and generalizability; developing real-time AR/MR systems that integrate simulation or optimization results into surgical workflows; and creating AI-powered platforms that learn from clinical data to provide adaptive and personalized flap design recommendations. These findings offer a pathway to bridge the gap between simulation and clinical practice, ultimately aiming to improve surgical outcomes.