Meccanica Magazine n. 4

meccanica magazine
30
research focuses on bone micro-scale, characterized by elliptical
micro-porosities (lacunae), whose role in damage mechanisms has
not been elucidated yet. Visualizing the architecture of the lacunar
network, investigating and simulating the onset of micro-damage
and finally predicting its evolution at the multi-scale, are factors of
great interest both from a biomechanical and a clinical perspective.
Which is GAP network?
In order to pursue its objectives, the synergy of competences is an
aspect of primary importance: GAP collaborates with a national and
international excellence network.
The project started from a collaboration with the Gruppo San
Donato without economic exchange (in self-financing); later the
other 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 microdamage
detection > 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 Alta
Scuola 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 students
developed a deep learning algorithm able to automatically detect
bone lacunae, leading to immediate processing of synchrotron
images.
What are GAP reached goals?
GAP project has achieved several encouraging results, which boost
its development. Currently, thanks to the mechanical and electronic
design and consequent realization of a micro-compression device,
it has been possible to map the local mechanical characteristics of
healthy and pathological femoral heads. This allows to determine
the areas with the highest Young’s modulus, where loads are
transmitted from the pelvis to the femur. Moreover, the implemented
methodology permit to evaluate the inter-patient variability,
identifying borderline cases of subjects affected by local arthrosis
or osteopenia. The mechanical characterization, combined with
synchrotron imaging at 1.6 µm resolution at increasing applied
displacement intervals, allow to estimate the interactions between
micro-cracks and lacunae, identifying toughening phenomena
such as ligament bridging. The high computational costs resulting
from extremely high-resolution data leads to the implementation
of convolutional neural networks for the automatic localization and
detection of micro-cracks and lacunae. We observe that, in all the
analyzed cases, micro- cracks do not originate from lacunae, that
present a higher density and an elliptical shape in presence of
osteoporosis.
What is the future of GAP?
Advanced synchrotron imaging techniques, together with local
mechanical characterization, allow to understand how micro-damage
develops within human bones. The obtained high-resolution images
permit the implementation of validated computational models, able
to predict the regions where there is a high risk of fracture, even
before it occurs. In addition, the identification of a micro-scale
fragility index, correlated with clinical imaging techniques, would
provide an additional and more specific tool to identify individuals
at risk of developing bone disease. Indeed, the early detection of an
increased propensity to bone fragility at the micro-scale level, would
allow the preventive administration of drugs to counteract the loss
of bone mineral density. In addition, this would lead to a delayed
hospitalization of many patients and a prolongation of their active
life. GAP, therefore, turns its gaze towards a practical approach with
a 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 process
of “learn and apply”. The scenarios collectively imagined for
FactoryBricks support the learner in the exploration, assembly, and
set-up of the production system, as well as reflection and critical
thinking. With the results obtained in this project, Politecnico di
Milano contributes to innovative teaching methodologies, which
will shape the way life-long learning will be done in the future, and
delivered not only to students, but also toward professional trainees
and the general public.