The mechanical characteristics of the female pelvic floor are relevant when explaining pelvic dysfunction. The decreased elasticity of the tissue often causes inability to maintain urethral position, also leading to vaginal and rectal descend when coughing or defecating as a response to an increase in the internal abdominal pressure. These conditions can be associated with changes in the mechanical properties of the supportive structures—namely, the pelvic floor muscles—including impairment. In this work, we used an inverse finite element analysis to calculate the material constants for the passive mechanical behavior of the pelvic floor muscles. The numerical model of the pelvic floor muscles and bones was built from magnetic resonance axial images acquired at rest. Muscle deformation, simulating the Valsalva maneuver with a pressure of 4 KPa, was compared with the muscle displacement obtained through additional dynamic magnetic resonance imaging. The difference in displacement was of 0.15 mm in the antero-posterior direction and 3.69 mm in the supero-inferior direction, equating to a percentage error of 7.0% and 16.9%, respectively. We obtained the shortest difference in the displacements using an iterative process that reached the material constants for the Mooney–Rivlin constitutive model ( c₁₀=11.8 KPa and c₂₀=5.53 E−02 KPa). For each iteration, the orthogonal distance between each node from the group of nodes which defined the puborectal muscle in the numerical model versus dynamic magnetic resonance imaging was computed. With the methodology used in this work, it was possible to obtain in vivo biomechanical properties of the pelvic floor muscles for a specific subject using input information acquired non-invasively.

Pelvic disorders can be associated with changes in the biomechanical properties in the muscle, ligaments and/or connective tissue form fascia and ligaments. In this sense, the study of their mechanical behavior is important to understand the structure and function of these biological soft tissues. The aim of this study was to establish the biomechanical properties of the pelvic floor muscles of continent and incontinent women, using an inverse finite element analysis (FEA). The numerical models, including the pubovisceral muscle and pelvic bones were built from magnetic resonance (MR) images acquired at rest. The numerical simulation of Valsalva maneuver was based on the finite element method and the material constants were determined for different constitutive models (Neo-Hookean, Mooney-Rivlin and Yeoh) using an iterative process. The material constants (MPa) for Neo-Hookean (c₁) were 0.039 ± 0.022 and 0.024 ± 0.004 for continent vs. incontinent women. For Mooney-Rivlin (c₁) the values obtained were 0.026 ± 0.010 vs. 0.016 ± 0.003, and for Yeoh (c₁) the values obtained were 0.031 ± 0.023 vs. 0.016 ± 0.002, (p < 0.05). Muscle displacements obtained in the numerical simulations of Valsalva maneuver were compared with the muscle displacements obtained through additional dynamic MRI. Incontinent women presented a higher antero-posterior displacement than the continent women. The results were also similar between MRI and numerical simulations (40.27% vs. 42.17% for Neo-Hookean, 39.87% for Mooney-Rivlin and 41.61% for Yeoh). Using an inverse FEA coupled with MR images allowed to obtain the in vivo biomechanical properties of the pelvic floor muscles, leading to a relationship between them for the continent and incontinent women in a non-invasive manner.

Synthetic implants were used for repair of anterior compartment prolapses, which can be caused by direct trauma resulting in damaged pelvic structures. The mechanical properties of these implants may cause complications, namely erosion of the mesh through the vagina. In this study, we evaluated, by modeling, the behavior of implants, during Valsalva maneuver, used to replace damaged uterosacral ligaments (USLs), mimicking a sacrocolpopexy repair. For this purpose, two synthetic implants (A, for prolapse repair and B, for Hernia repair) were uniaxially tested, and the mechanical properties obtained were incorporated in the computational models of the implants. The computational model for the implant was incorporated into the model of the female pelvic cavity, in order to mimic the USLs after its total rupture and with 90% and 50% impairment. The total rupture and impairments of the USLs, caused a variation of the supero-inferior displacement and displacement magnitude of the vagina, with higher values for the total rupture. With total rupture of the USLs, when compared to healthy USLs, supero-inferior displacement and displacement magnitude of the vagina increased by 4.98 mm (7.69 mm vs 12.67 mm) and 6.62 mm (9.38 mm vs 16.00 mm), respectively. After implantation (A and B) a reduction of the supero-inferior displacements of the anterior vaginal wall occurred, to values found in the case of the model without any impairment or rupture of the ligaments. The simulation was able to mimic the biomechanical response of the USLs, in response to different implants stiffnesses, which can be used in the development of novel meshes.

Hernia is a physiological condition that significantly impacts patients’ quality of life. Surgical treatment for hernias often involves the use of specialized meshes to support the abdominal wall. While this method is highly effective, it frequently leads to complications such as pain, infections, inflammation, adhesions, and even the need for revision surgeries. According to the Food and Drug Administration (FDA), hernia recurrence rates can reach up to 11%, surgical site infections occur in up to 21% of cases, and chronic pain incidence ranges from 0.3% to 68%. These statistics highlight the urgent need to improve mesh technologies to minimize such complications. The design and material composition of meshes are critical in reducing postoperative complications. Moreover, integrating drug-eluting properties into the meshes could address issues like infections and inflammation by enabling localized delivery of antibiotics and anti-inflammatory agents. Mesh design is equally important, with innovative structures like auxetic designs offering enhanced mechanical properties, flexibility, and tissue integration. These advanced designs can distribute stress more evenly, reduce fatigue, and improve performance in areas subjected to high pressures, such as during intense coughing, sneezing, or heavy lifting. Technological advancements, such as 3D printing, enable the precise fabrication of meshes with tailored designs and properties, providing new opportunities for innovation. By addressing these challenges, the development of next-generation mesh implants has the potential to reduce complications, improve patient outcomes, and significantly enhance quality of life for individuals undergoing hernia repair.

To better understand the disorders in the pelvic cavity associated with the pelvic floor muscles (PFM) using computational models, it is fundamental to identify the biomechanical properties of these muscles. For this purpose, we implemented an optimization scheme, involving a genetic algorithm (GA) and an inverse finite element analysis (FEA), in order to estimate the material properties of the pubovisceralis muscle (PVM). The datasets of five women were included in this noninvasive analysis. The numerical models of the PVM were built from static axial magnetic resonance (MR) images, and the hyperplastic Mooney–Rivlin constitutive model was used. The material parameters obtained were compared with the ones established through a similar optimization scheme, using Powell's algorithm. To validate the values of the material parameters that characterize the passive behavior of the PVM, the displacements obtained via the numerical models with both methods were compared with dynamic MR images acquired during Valsalva maneuver. The material parameters (c₁ and c₂) were higher for the GA than for Powell's algorithm, but when comparing the magnitude of the displacements in millimeter of the PVM, there was only a 5% difference, and 4% for the principal logarithmic strain. The GA allowed estimating the in vivo biomechanical properties of the PVM of different subjects, requiring a lower number of simulations when compared to Powell's algorithm.