Research Center of Biomedical Engineering - RCBE
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Background: The knee is one of the most common areas to suffer injuries or be affected by surgery. Physiotherapy rehabilitation was shown to support recovery, but evidence guiding optimal rehabilitation practices is limited. To recommend appropriate exercises, it is essential to understand the musculoskeletal requirements involved in both physiotherapy and activities of daily living (ADLs).ObjectiveThis study aimed to evaluate and compare the knee joint kinematics, joint forces and muscle activity in knee flexors and extensors during selected rehabilitation exercises and ADLs.MethodsKinematic and kinetic data from 30 healthy participants were collected during 20 different tasks. Full-body musculoskeletal simulations were performed to estimate peak knee joint angles, angular velocities, joint reaction forces, and muscle activity of the knee flexors and extensors.ResultsComparatively high requirements were observed for lunges, squats, stair walking and gait. Medium requirements were observed for sitting down and rising from a chair. Low requirements were observed for balance shifts and variations of the single leg stand.ConclusionOverall, ADLs like gait and stair walking show surprisingly high requirements compared to many exercises employed in physiotherapy. These findings are a step towards biomechanically informed exercise selection and the development of personalized rehabilitation programs.
Numerical modeling is a valuable tool to research shear-induced aerosol generation inside the human respiratory system. While the volume of fluid method and Eulerian wall film models have been used to predict the stripping of particles from the mucus film, sufficient validation data is lacking. Here, we present an experimental method to create such validation data. A film of mucus mimetic hydrogel with an initial thickness of 1 mm covering the floor of a rectangular channel (75.5 mm 25.5 mm 3 mm) was exposed to an airflow with a flow rate of 9.5 and 21.6 L/min. The number of created particles and the emergence of waves on the mucus surface were measured. Shear-induced aerosol generation was triggered successfully and caused an increase of mean particle flow. Different wave profiles were observed at varying film depths.
Automated deep learning based detection of cellular deposits on clinically used ECMO membrane lungs
(2026)
Introduction:
Despite the promising application of extracorporeal membrane oxygenation (ECMO) in the treatment of critically ill patients, coagulation-associated technical complications, primarily clot formation and critical bleeding, remain a major challenge during ECMO therapy. The deposition of nucleated cells on the surface has been shown, yet the role of these cells towards complication development is still matter of ongoing research. In particular, the membrane lung (MemL) is prone to clot formation. Therefore, the investigation of nuclear deposits on its hollow-fibers may provide insights for a better understanding of the cellular mechanisms involved in the development of ECMO complications.
Methods:
To support current research, this study aimed to develop a deep learning–based tool for the automated detection and quantitative analysis of nuclear depositions on MemL hollow-fiber mats. A customized fluorescence microscopy workflow, combined with a semi-automated iterative labeling strategy, was used to generate a high-quality dataset for model training.
Results:
Six configurations of instance segmentation models were evaluated, with a Mask R-CNN with ResNet 101 backbone using dilated convolution providing the most balanced performance in both nuclei count and area accuracy. Compared with U-Net–based approaches such as Cellpose or StarDist, the proposed model demonstrated superior segmentation of overlapping and low-intensity nuclei, maintaining accuracy even in densely packed cellular regions.
Discussion:
We present an automated image analysis tool for clinically used MemLs, which exhibit complex three-dimensional hollow-fiber architectures and irregular cellular deposits that challenge conventional tools. A dedicated graphical user interface enables streamlined detection, morphometric analysis, and spatial clustering of nuclei, establishing a reproducible workflow for high-throughput analysis of fluorescence microscopy images. This approach eliminates labor-intensive manual counting and facilitates large-scale studies on cell-fiber interactions and disease-related correlations.
The high cost of challenge platforms prevents many people from organizing their own competitions. The do-it-yourself (DIY) challenge blueprint [1] allows you to host your own biomedical AI benchmark challenge. Our DIY approach circumvents the current constraints of commercial challenge platforms. A sovereign, extensible and cost-efficient deployment is provided via containerised, identity-managed and reproducible pipelines. Focus lies on GDPR-compliant hosting via infrastructure-as-code, automated evaluation, modular orchestration, and role-based identity and access management. The framework integrates Docker-based execution and standardised interfaces for task definitions, dataset curation and evaluation. All in all it is designed to be flexible and modular, as demonstrated in the MICCAI 2024 PhaKIR challenge [2, 3]. In this case study, different medical tasks on a multicentre laparoscopic dataset with framewise labels for phases and spatial annotations for instruments across fulllength videos were supported. This case study empirically validates the DIY challenge blueprint as a reproducible and customizable challenge-hosting infrastructure. The full code can be found at https://github.com/remic-othr/PhaKIR_DIY.
High-resolution flow field investigations in membrane lungs, considering the complex blood rheology
(2024)
Despite major improvements over the last years, coagulative disorders and clotting
phenomena in membrane lungs (MLs) are still considerable complications in extracorporeal
membrane oxygenation (ECMO). ECMO is an increasingly used treatment for patients with
severe respiratory failure or cardiac arrest [1]. For both, evaluation of therapeutic decisions
and fundamental research on patient specific intra-device clotting phenomena, the direct
visualization and analysis of clot formation in combination with a detailed flow field
correlation is highly desirable and therefore an intensively followed research topic.
Modelling blood flow and shear induced coagulation in MLs is challenging. The relevant
geometry of oxygenator fibers and chaining threads is complex and spans several length scales.
In relevant scales and regimes, blood shows several significant non-Newtonian effects.
Viscosity impacts shear rate, which is important in several coagulation mechanisms.
Additionally, coagulation processes are influencing fluid properties and geometry
significantly. Existing approaches of previous research work are only able to consider some,
but not all relevant effects and geometrical details. Due to the enormous size of the discretized
geometries, highly detailed viscosity and coagulations models are not applicable. Our goal is
to develop a model for combined viscosity and coagulation properties of blood flow in MLs.
In our work, we compare the influence of different levels of detail of the ML geometry as well
as the influence of considering realistic blood flow behavior (viscosity change by considering
the local hematocrit distribution within the Fåhraeus-Lindqvist-Effect) on the resulting flow
field in relevant subsections of a ML.
High-resolution micro-CT geometry reconstructions [1] are compared to idealized generic
fiber representations. For realistic blood flow modelling, Newtonian representation is
compared to the established Carreau-Yasuda and a multiphase Euler-Euler approach.
Results are presented for relevant subsections as well as for the complete ML.
This book offers timely insights into research on numerical and experimental fluid mechanics and aerodynamics. It reports on findings by members of the Deutsche Strömungsmechanische Arbeitsgemeinschaft, STAB (German Aerodynamics/Fluid Mechanics Association) and the Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal Oberth e.V., DGLR (German Society for Aeronautics and Astronautics) and covers both nationally and EC-funded projects. Continuing on the tradition of the previous volumes, the book highlights innovative solutions, promoting translation from fundamental research to industrial applications. It addresses academics and professionals in the field of aeronautics, astronautics, ground transportation, and energy alike.
Complex blood flow phenomena in membrane lungs (MLs) play a crucial role in intra-device clot formation and the occurrence of thromboembolic events. At present, however, the local flow conditions within an ML are not yet sufficiently known. The aim was to gain a deeper understanding of local flow regimes inside MLs by performing highly resolved computational fluid dynamics (CFD) of generic and native fiber mat bundles. Straight cylinders with a diameter of 380 μm in parallel arrangement were the foundation of the generic model. For validation, a method for reconstructing a native geometry from a microcomputed tomography (μCT) scan was established, with both models used for CFD. While the generic model showed a symmetrical flow regime without indicating any pathological flow, the native model did show an irregular fiber arrangement and no symmetrical flow regime. In conclusion, the fiber arrangement significantly affects the local flow regimes inside MLs.