meccanica magazine30research focuses on bone micro-scale, characterized by ellipticalmicro-porosities (lacunae), whose role in damage mechanisms hasnot been elucidated yet. Visualizing the architecture of the lacunarnetwork, investigating and simulating the onset of micro-damageand finally predicting its evolution at the multi-scale, are factors ofgreat interest both from a biomechanical and a clinical perspective.Which is GAP network?In order to pursue its objectives, the synergy of competences is anaspect of primary importance: GAP collaborates with a national andinternational excellence network.The project started from a collaboration with the Gruppo SanDonato without economic exchange (in self-financing); later theother partners joined.Bone mechanical and biological characterization at multiscale >ELETTRA Synchrotron (prof. Tromba), EMPA (prof. Schwiedrzik),University of Strasbourg (prof. Carradò), University of Eindhoven(prof. Hofmann).Numerical modeling of bone damage > Trinity College Dublin (prof.Taylor), Dioscuri Centre in Topological Data Analysis (prof. Dlotko),ETH Zurich (prof. Müller), TU Delft (prof. Zadpoor, prof. Mirzaali)From the research to the clinics: artificial intelligence for microdamagedetection > NTNU (prof. Berto)Clinical partners and social impact > Istituto Ortopedico Galeazzi(prof. Banfi) and Cittadinanzattiva (dr. Nicoletti).A sub-section of GAP research is performed in collaboration with AltaScuola Politecnica, obtaining promising results (https://poliflash.polito.it/ricerca_e_innovazione/progetto_gap_un_passo_avanti_nella_prevenzione_delle_fratture_ossee ). A group of five studentsdeveloped a deep learning algorithm able to automatically detectbone lacunae, leading to immediate processing of synchrotronimages.What are GAP reached goals?GAP project has achieved several encouraging results, which boostits development. Currently, thanks to the mechanical and electronicdesign and consequent realization of a micro-compression device,it has been possible to map the local mechanical characteristics ofhealthy and pathological femoral heads. This allows to determinethe areas with the highest Young’s modulus, where loads aretransmitted from the pelvis to the femur. Moreover, the implementedmethodology permit to evaluate the inter-patient variability,identifying borderline cases of subjects affected by local arthrosisor osteopenia. The mechanical characterization, combined withsynchrotron imaging at 1.6 µm resolution at increasing applieddisplacement intervals, allow to estimate the interactions betweenmicro-cracks and lacunae, identifying toughening phenomenasuch as ligament bridging. The high computational costs resultingfrom extremely high-resolution data leads to the implementationof convolutional neural networks for the automatic localization anddetection of micro-cracks and lacunae. We observe that, in all theanalyzed cases, micro- cracks do not originate from lacunae, thatpresent a higher density and an elliptical shape in presence ofosteoporosis.What is the future of GAP?Advanced synchrotron imaging techniques, together with localmechanical characterization, allow to understand how micro-damagedevelops within human bones. The obtained high-resolution imagespermit the implementation of validated computational models, ableto predict the regions where there is a high risk of fracture, evenbefore it occurs. In addition, the identification of a micro-scalefragility index, correlated with clinical imaging techniques, wouldprovide an additional and more specific tool to identify individualsat risk of developing bone disease. Indeed, the early detection of anincreased propensity to bone fragility at the micro-scale level, wouldallow the preventive administration of drugs to counteract the lossof bone mineral density. In addition, this would lead to a delayedhospitalization of many patients and a prolongation of their activelife. GAP, therefore, turns its gaze towards a practical approach witha strong social impact, interfacing directly not only with clinicians,but also with patients, the end users of its research.solving approach enables trainees to go beyond the basic processof “learn and apply”. The scenarios collectively imagined forFactoryBricks support the learner in the exploration, assembly, andset-up of the production system, as well as reflection and criticalthinking. With the results obtained in this project, Politecnico diMilano contributes to innovative teaching methodologies, whichwill shape the way life-long learning will be done in the future, anddelivered not only to students, but also toward professional traineesand the general public.
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