Project description DEENESFRITPL A model for predicting bone healing When it comes to bone fractures, existing assessment tools lack the resolution to identify and isolate the initial fracture sites, especially if these are located in the distal part of the bone known as metaphysis. The aim of the EU-funded HealinguFE project is to develop a tool for identifying such microfractures and predicting their healing evolution. Using high-resolution peripheral computed tomography data from patients with wrist fractures, scientists will develop an in silico model capable of detecting structural changes in the bone. The generated tool will advance existing knowledge on the mechanisms underlying trabecular bone healing and fuel future research towards novel interventions. Show the project objective Hide the project objective Objective Current knowledge of fracture healing is based on experimental animal studies of diaphyseal bone, despite 20% of fractures occurring in the metaphysis of the distal forearm. Moreover, current fracture healing assessment tools lack the resolution to identify and isolate the initial fracture site in cases involving a crushing fracture. The objectives of this project are to (1) develop a micro-finite element (μFE) model for fracture site identification, (2) develop an in silico model for human bone fracture healing capable of tracking local microstructural changes, and (3) test the predictive power of the in silico model using clinical data. μFE models will be generated from high-resolution peripheral computed tomography (HRpQCT) data of the distal radius from wrist fracture patients. The μFE models will be generated from HRpQCT data collected at the early stages of fracture healing and compared to remodelling maps in order to determine if μFE models can be used to isolate the site of the initial fracture. Adaptations will be made to an existing in silico model based on the findings of the μFE analysis. The resulting in silico model will be applied to clinical HRpQCT data to predict endpoint microstructural changes as well as patterns in fracture healing and remodelling. The predictive power of this in silico model will be determined by comparing the simulation results to observed behavior in vivo. The proposed project merges recent advances in bone mechanobiology, μFE simulations, and medical imaging to develop novel image analysis and registration methods as well as a tool for predicting if, when, and where fracture healing will occur. The proposed work will provide insights into the local behavior of trabecular bone fracture healing and help the fellow achieve professional maturity. Further, the in silico model has the potential to change the landscape of fracture healing research, particularly in areas of preclinical testing and personalized medicine. Fields of science engineering and technologymedical engineeringdiagnostic imagingcomputed tomographymedical and health scienceshealth sciencespersonalized medicine Keywords Fracture site identification bone healing mechanobiology micro finite element analysis in silico modeling Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2018 - Individual Fellowships Call for proposal H2020-MSCA-IF-2018 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH Net EU contribution € 191 149,44 Address Raemistrasse 101 8092 Zuerich Switzerland See on map Region Schweiz/Suisse/Svizzera Zürich Zürich Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
