Open Access

Introduction Pelvic floor disorders affect about 40% of women worldwide [1]. Pelvic floor muscle (PFM) training is both a preventive and a therapeutic intervention. Current PFM training devices are invasive and have little scientific evidence. The idea is to develop a noninvasive feedback device to assess adequate PFM contraction. Therefore, evidence-based female musculoskeletal models, non-invasive data acquisition, sensor technology and artificial intelligence (AI) will be combined. This work presents the workflow to achieve such a feedback device and describes the interaction of the technologies used. Methods Exercises that induce PFM contractions have been evaluated and defined. Motion capture of these exercises will provide input for female musculoskeletal models. A combination of biomechanical rigid body and FEM simulations will be used to estimate PFM contractions. In addition, a non-invasive sensor will measure pelvic floor activity. The simulated and measured data will be used to develop an AI model that provides feedback on PFM contractions based on non-invasive data collection. Results The AMMR (AnyBody Managed Model Repository) of the AnyBody modelling system (AMS, Aalborg, Denmark) serves as the initial model for performing inverse dynamic simulations of the exercises. To calculate the PFM forces, the full-body model must be supplemented with the relevant pelvic floor structures and a mass model of the internal organs. A modified abdominal pressure model must also be incorporated. The AMS calculates the PFM activities caused by the internal organ loads and the generated abdominal pressure during the exercises. The muscle activities are transferred to a FEM model of the female pelvic floor (SfePy, simple finite elements in Python). The identical pelvic floor structures were integrated into the FEM model as in the AMS. Active PFM contractions can be simulated using the FEM model. Movement of the coccyx due to PFM contractions has been reported in the literature [2,3]. Therefore, a noninvasive coccyx motion sensor will be developed to provide additional information on PFM contractions. The measured data (coccyx motion sensor, motion capture) and the simulation results of the models will be combined to create an AI feedback model using Python. The final feedback device will consist of the AI model and the developed coccyx motion sensor, which can reproduce the resulting PFM contractions based on the sensor data and simplified motion tracking. Discussion The creation of the AMS and the FEM model is a prerequisite for the development of the feedback device. The relevant structures in the models are located inside the body. This limits the ability to observe the structures during the exercises, which can lead to difficulties in model validation. The development of a user-friendly sensor with sufficient measuring accuracy of the coccyx motion is another challenge. Nevertheless, the workflow represents a promising approach to develop a noninvasive feedback system to assess PFM contraction. References 1. Wang et al, Front Public Health, 10:975829, 2022. 2. Bø et al, Neurourol Urodyn, 20:167–174, 2001. 3. Fujisaki et al, J Phys Ther Sci, 30:544–548, 2018. Acknowledgements This work was supported by the project no. BYCZ01-014 of the Program INTERREG Bavaria – Czechia 2021–2027.

The success of surgical treatment for fractures hinges on various factors, notably accurate surgical indication. The process of developing and certifying a new osteosynthesis device is a lengthy and costly process that requires multiple cycles of review and validation. Current methods, however, often rely on predecessor standards rather than physiological loads in specific anatomical locations. This study aimed to determine actual loads experienced by an osteosynthesis plate, exemplified by a standard locking plate for the femoral shaft, utilizing finite elements analysis (FEA) and to obtain the bending moments for implant development standard tests. A protocol was developed, involving the creation and validation of a fractured femur model fixed with a locking plate, mechanical testing, and FEA. The model’s validation demonstrated exceptional accuracy in predicting deformations, and the FEA revealed peak stresses in the fracture bridging zone. Results of a parametric analysis indicate that larger fracture gaps significantly impact implant mechanical behavior, potentially compromising stability. This study underscores the critical need for realistic physiological conditions in implant evaluations, providing an innovative translational approach to identify internal loads and optimize implant designs. In conclusion, this research contributes to enhancing the understanding of implant performance under physiological conditions, promoting improved designs and evaluations in fracture treatments.

Extracorporeal membrane oxygenation (ECMO) was established as a treatment for severe cardiac or respiratory disease. Intra-device clot formation is a common risk. This is based on complex coagulation phenomena which are not yet sufficiently understood. The objective was the development and validation of a methodology to capture the key properties of clots deposed in membrane lungs (MLs), such as clot size, distribution, burden, and composition. One end-oftherapy PLS ML was examined. Clot detection was performed using multidetector computed tomography (MDCT), microcomputed tomography (μCT), and photography of fiber mats (fiber mat imaging, FMI). Histological staining was conducted for von Willebrand factor (vWF), platelets (CD42b, CD62P), fibrin, and nucleated cells (4′, 6-diamidino-2-phenylindole, DAPI). The three imaging methods showed similar clot distribution inside the ML. Independent of the imaging method, clot loading was detected predominantly in the inlet chamber of the ML. The μCT had the highest accuracy. However, it was more expensive and time consuming than MDCT or FMI. The MDCT detected the clots with low scanning time. Due to its lower resolution, it only showed clotted areas but not the exact shape of clot structures. FMI represented the simplest variant, requiring little effort and resources. FMI allowed clot localization and calculation of clot volume. Histological evaluation indicated omnipresent immunological deposits throughout the ML. Visually clot-free areas were covered with leukocytes and platelets forming platelet-leukocyte aggregates (PLAs). Cells were embedded in vWF cobwebs, while vWF fibers were negligible. In conclusion, the presented methodology allowed adequate clot identification and histological classification of possible thrombosis markers such as PLAs.