Intra-abdominal bleeding is a leading cause of adverse outcomes in liver trauma, underscoring the critical importance of timely surgical intervention and optimal hemostasis strategies. This study investigates the impact of liver model segmentation on the distribution of mechanical stresses during impact simulation using the finite element method. By discretizing a computer-aided design (CAD) model into finite elements of two types – 4-node linear isoparametric elements for the parenchyma and 3-node Kirchhoff plate elements for intersegmental partitions – we developed a computational liver model adequately captures the organ's anatomy. Our results show that incorporating segmentation significantly affects the simulation outcomes, enabling a more realistic assessment of tissue damage patterns upon impact. This approach facilitates the simulation of a wide range of real-world traumatic effects, allowing for arbitrary specification of impact direction, speed, and affected area. By accounting for liver segmentation, our study provides a more accurate and comprehensive understanding of traumatic liver injuries, ultimately informing clinical decision-making and improving patient outcomes.
Intra-abdominal bleeding is a leading cause of adverse outcomes in liver trauma, underscoring the critical importance of timely surgical intervention and optimal hemostasis strategies. This study investigates the impact of liver model segmentation on the distribution of mechanical stresses during impact simulation using the finite element method. By discretizing a computer-aided design (CAD) model into finite elements of two types – 4-node linear isoparametric elements for the parenchyma and 3-node Kirchhoff plate elements for intersegmental partitions – we developed a computational liver model adequately captures the organ's anatomy. Our results show that incorporating segmentation significantly affects the simulation outcomes, enabling a more realistic assessment of tissue damage patterns upon impact. This approach facilitates the simulation of a wide range of real-world traumatic effects, allowing for arbitrary specification of impact direction, speed, and affected area. By accounting for liver segmentation, our study provides a more accurate and comprehensive understanding of traumatic liver injuries, ultimately informing clinical decision-making and improving patient outcomes.
The authors express their deep gratitude to Alexey Andreevich Olshevsky, Candidate of Technical Sciences, Associate Professor, for his help in preparing the model and performing calculations. The work was carried out without external sources of funding.