The aim of the project is to face bone fractures crisis adopting a twofold approach, considering both fracture prevention and treatment.
On the prevention side, the main goal is to work for the implementation of a microscale fragility index for fracture prevention to be used in clinical practice to estimate the probability of fractures. To achieve this result, it is necessary to first identify current clinical parameters used to define bone fragility and macro and micro scale effects of bone pathologies on bone architecture. Microarchitecture analysis would be assisted by the development of an Artificial Neural Network (ANN) for the recognition of micro-defects and damages on high-resolution synchrotron images of human bones.
Regarding the treatment side, the objective is to provide strategies to design and develop tailor-made 3D-printed bone substitutes for bone regeneration, to be produced with advanced additive manufacturing techniques, made by a scaffold seeded with mesenchymal stem cells (MSCs), which will focus the healing process where it is most needed, whilst providing adequate mechanical properties similar to the host bone. To ensure a better recovery, scaffold morphology needs to be optimized and customized according to the patient’s specific needs. This could be possible after having investigated the influence of scaffold architecture on bone tissue growth thanks to the development of AI tools to be applied to synchrotron bone constructs images. The Artificial Neural Network would focus on the recognition of different regions of the scaffold, based on geometrical and structural parameters favoring bone regeneration and providing mechanical strength.
Our work could provide a methodology for the microscale study for several bone conditions, ranging from osteoporosis to rare bone diseases, with concrete benefits not only for patients but ultimately for the health sector and the entire society.