Thematic Axis: Bioengineering, Computational Modeling, and AI for Medical Innovation

Thematic Axis: Bioengineering, Computational Modeling, and AI for Medical Innovation

The "Bioengineering, Computational Modeling, and AI for Medical Innovation" thematic axis promotes interdisciplinary collaborations at the interface of numerical simulation and bioengineering, with the goal of developing new healthcare technologies and innovative medical devices.

Application Domains

CEMEF’s work in this field covers various medical areas:

• Neurovascular: cerebral aneurysms
• Cardiovascular: heart failure, coronary aneurysms
• Respiratory: acute respiratory distress syndrome, empty nose syndrome
• Maxillofacial and dental: malocclusion, implantology
• Orthopedics: anterior cruciate ligament rupture, complex fracture reduction
• Dermatology and cosmetics

Research efforts also extend to the development of various medical devices, ensuring mechanical (stents, cardiac pumps) or physical actions (dental biomaterials, bone substitutes, bio-aerogels for drug release in dressings or oral administration, hydrogels for regenerative medicine).

Actions and Innovations

The research activities are diverse and aim to address emerging needs:

• Development of advanced numerical methods in solid and fluid mechanics, enabling precise simulations of biological material behavior (blood, vessels, bones, ligaments, biomaterials) in complex medical applications, considering strong mechanical and physico-chemical coupling at interfaces.
• Characterization and modeling of biological materials, whose specific properties (density, stiffness, resistance, reactivity) require tailored approaches. These studies rely on various experimental techniques: static and dynamic mechanical testing, microfluidics, rheology, optical and electron microscopy, UV-visible spectrophotometry, and 3D printing (DIW, SLA).
• High-performance simulation of personalized models, integrating 3D/4D imaging from MRI and CT scans, covering the entire process from image segmentation to 3D meshing.
• Validation and improvement of models through comparison with experimental data, correlating in silico simulations with in vitro measurements on 3D-printed models.
• Integration of AI at all levels towards digital twin technology, notably for automatic segmentation of medical images, rapid model prediction, and optimized medical device design through machine learning.

This synergy between experimental characterization, numerical simulation, and artificial intelligence provides a powerful approach to explore complex phenomena at various scales and optimize device performance. It allows, for example, adjusting the composition of a porous biomaterial to match a bone defect’s morphology, tailoring the design of an endoprosthesis to vascular anatomy, or optimizing the biological properties of a polymer for specific applications.