TY - JOUR
T1 - Finite element analysis-assisted surgical planning and evaluation of flap design in hand surgery
T2 - Frontiers in Bioengineering and Biotechnology
AU - Yang, Guang
AU - Shen, Hui
AU - Jang, Yewon
AU - Cheng, Xiangyi
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - augmented reality
KW - finite element analysis
KW - finite element method
KW - flap design
KW - hand surgery
KW - mixed reality
KW - surgical evaluation
KW - surgical planning
UR - https://www.scopus.com/pages/publications/105008873226
UR - https://www.scopus.com/pages/publications/105008873226#tab=citedBy
U2 - 10.3389/fbioe.2025.1611993
DO - 10.3389/fbioe.2025.1611993
M3 - Article
C2 - 40567254
SN - 2296-4185
VL - 13
JO - Front. Bioeng. Biotechnol.
JF - Front. Bioeng. Biotechnol.
ER -