image-Guided computational and experimental Analysis of fractured Patients

Project Information

GAP

Grant agreement ID: 101120290

DOI

10.3030/101120290

EC signature date 22 June 2023

Start date 1 October 2023

End date 31 May 2028

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 2 679 933,60

Coordinated by

POLITECNICO DI MILANO
Italy

Periodic Reporting for period 1 - GAP (image-Guided computational and experimental Analysis of fractured Patients)

Reporting period: 2023-10-01 to 2025-09-30

Summary of the context and overall objectives of the project

Bone fractures, particularly those related to bone fragility, represent a growing challenge for European societies. Increasing life expectancy and age-related conditions such as osteoporosis are driving a rise in fragility fractures, which often result in pain, long recovery periods, loss of independence and reduced quality of life. These injuries also place a substantial burden on healthcare systems and public finances. Despite advances in imaging and treatment, fractures are frequently detected too late, and current diagnostic tools remain limited in accurately predicting fracture risk.
The GAP project (image-Guided computational and experimental analysis of fractured Patients) was designed to address this urgent medical and societal need. Its central aim is to improve how fractures are understood, detected and treated by focusing on bone damage at very small scales, well before complete fractures occur. Bone strength depends not only on shape and density but also on its complex internal micro-architecture, which is largely invisible to standard clinical diagnostics.
To overcome this limitation, GAP integrates advanced imaging, experimental testing, computational modelling and artificial intelligence within a single research and training framework. Bone damage is studied across multiple length scales using high-resolution imaging combined with mechanical testing under realistic conditions, and the resulting observations are translated into numerical and data-driven tools to identify early damage and fracture risk patterns.
In parallel, GAP trains a new generation of highly skilled researchers through an international doctoral network that bridges engineering, biomedical sciences, computer science and clinical research. This interdisciplinary approach prepares young scientists to translate research outcomes into practical solutions.
Beyond diagnosis, the project also explores bone-inspired, mini-invasive and patient-specific repair strategies, with the potential to improve fracture healing and long-term outcomes. Embedded in the broader European context of healthy ageing and sustainable healthcare, GAP contributes to strengthening Europe’s capacity to prevent fractures, enhance patient care and reduce the social and economic impact of bone fragility.
Further information about the project is available on:
i) the project website: https://www.gapmscaproject.com/
ii) LinkedIn profile: https://www.linkedin.com/in/gap-msca-project-72a536316/
iii) Instagram profile: https://www.instagram.com/gap_msca_project/

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the first reporting period, the project carried out integrated experimental, computational and data-driven research to study bone damage mechanisms across multiple length scales. A major scientific achievement was the design, realization and successful use of a novel mechanical testing system compatible with high-resolution imaging, enabling direct observation of damage initiation and propagation in human bone samples under mechanical loading. This system was applied in experimental campaigns on healthy and pathological samples, generating high-quality multi-scale datasets.
In parallel, numerical and fracture-mechanics-based models were developed directly from experimental images, allowing simulation of stress distribution and damage evolution in bone. Initial validation against experimental observations confirmed the feasibility of the modelling approach. The project also implemented automated analysis methods, including artificial intelligence-based techniques, to detect and quantify micro-scale structural features and damage patterns, with early results differentiating between healthy and pathological conditions.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

During the first reporting period, the GAP project achieved key foundational scientific and technical results that establish the basis for its long-term objectives. The main focus of this phase was the development and validation of experimental, computational and analytical frameworks for studying bone damage across multiple length scales.
A major achievement was the development and successful use of a novel mechanical testing system compatible with high-resolution imaging, enabling direct observation of bone damage during mechanical loading. This capability was applied to healthy and pathological human bone samples, generating high-quality multi-scale datasets that provide new insight into early damage mechanisms.
The project also delivered initial numerical and fracture-mechanics-based models derived from experimental images, allowing simulation of stress distribution and damage evolution in bone. While full validation is planned for later phases, the results obtained so far confirm the feasibility and consistency of the modelling approach. In parallel, automated data analysis methods, including artificial intelligence-based techniques, were implemented to detect and quantify micro-scale bone features and damage patterns, with early analyses already distinguishing between healthy and pathological samples.
In addition, the first reporting period laid the scientific groundwork for bone repair concepts inspired by natural bone micro-architecture, defining initial design principles to be further developed in subsequent phases.

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