Application of classification algorithms for hip implant surface topographies

Experimental studies have shown that lower shear stress values lead to better femoral bone – hip implant connection. Numerical simulations have provided option to reduce the number of experimental studies through analysis of different hip implant surface topographies. However, this approach takes time as there are different model parameters that should be considered in order to understand how they affect the obtained shear stress values. The use of classification algorithms is an approach that could reduce the time required for simulation by providing information about models with biggest potential. Eleven model parameters related to model and surface topography were considered in combination with four classification algorithms - Support Vector Machines (SVM), K - Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The considered parameters were: Number of half-cylinders lengthwise (>0); Number of half-cylinder rows (≥0); Half cylinders added or removed from the surface (0 – removed; 1 - added); Distance between half-cylinders lengthwise (≥0); Distance between half-cylinders widthwise (≥0); Number of different radius values (1 or 2); Radius 1 value (>0); Radius 2 value (≥0); Distance from the edge where loading is located (≥0); Distance from the other edge of the model (≥0); Model includes trabecular bone (0 – not included; 1 - included). The aim was to apply previously mentioned algorithms to obtain information if the maximum shear stress value was above or below user-defined threshold. The obtained results show that this approach can be useful to obtain preliminary information about models that should be numerically analyzed.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

SGABU computational platform for multiscale modeling:Bridging the gap between education and research

BACKGROUND AND OBJECTIVE: In accordance with the latest aspirations in the field of bioengineering, there is a need to create a web accessible, but powerful cloud computational platform that combines datasets and multiscale models related to bone modeling, cancer, cardiovascular diseases and tissue engineering. The SGABU platform may become a powerful information system for research and education that can integrate data, extract information, and facilitate knowledge exchange with the goal of creating and developing appropriate computing pipelines to provide accurate and comprehensive biological information from the molecular to organ level. METHODS: The datasets integrated into the platform are obtained from experimental and/or clinical studies and are mainly in tabular or image file format, including metadata. The implementation of multiscale models, is an ambitious effort of the platform to capture phenomena at different length scales, described using partial and ordinary differential equations, which are solved numerically on complex geometries with the use of the finite element method. The majority of the SGABU platform's simulation pipelines are provided as Common Workflow Language (CWL) workflows. Each of them requires creating a CWL implementation on the backend and a user-friendly interface using standard web technologies. Platform is available at https://sgabu-test.unic.kg.ac.rs/login. RESULTS: The main dashboard of the SGABU platform is divided into sections for each field of research, each one of which includes a subsection of datasets and multiscale models. The datasets can be presented in a simple form as tabular data, or using technologies such as Plotly.js for 2D plot interactivity, Kitware Paraview Glance for 3D view. Regarding the models, the usage of Docker containerization for packing the individual tools and CWL orchestration for describing inputs with validation forms and outputs with tabular views for output visualization, interactive diagrams, 3D views and animations. CONCLUSIONS: In practice, the structure of SGABU platform means that any of the integrated workflows can work equally well on any other bioengineering platform. The key advantage of the SGABU platform over similar efforts is its versatility offered with the use of modern, modular, and extensible technology for various levels of architecture.</p