Identification of the homeostatic bone-remodelling stimulus from medical images

Computational methods enable the effective processing of medical data and the modelling of complex biological systems, thereby supporting the development of Personalized Medicine over recent decades. In this context, bone remodelling simulation is a promising tool, as the prediction of patient-specific bone adaptation may have multiple clinical and experimental applications in bone disease assessment and management. Numerical models combining the Finite Element Method (FEM) with bone remodelling algorithms can be generated from medical data such as CT scans. However, their accuracy strongly depends on how realistically bone properties and boundary conditions are represented. Because case-specific data are often difficult to obtain, generalized assumptions are commonly introduced, which can compromise simulation accuracy. This work aims to improve the personalization of bone remodelling simulations through the identification of the attractor stimulus (Ψ_A), a key parameter in bone remodelling analysis. To this end, we propose what is, to our knowledge, the first fully numerical and patient-specific methodology to identify Ψ_A directly from medical images.

Three approaches for identifying Ψ_A were explored. Method I was based on the assumption of homeostasis observable in medical imaging. Method II proposed a simplified methodology grounded in Mechanostat theory. Method III was designed as an in silico analogy of an in vivo experiment in order to illustrate the limitations associated with that type of experimental approach. All proposed methods were designed to rely only on a patient CT scan and body weight, thus avoiding the need for additional patient-specific measurements and supporting a clinically feasible workflow.

Applied to a human femur case study, Methods I and II yielded subject-specific Ψ_A values below the generic literature reference (of the order of 30-40 vs 50 MPa/day), whereas the in silico replication of the single-gauge experiment (Method III) produced markedly higher and probe-position-sensitive values, indicating that it is the least reliable of the three. These results suggest that CT-based information combined with patient weight may be a good starting point to estimate this key parameter without additional invasive measurements, supporting a clinically feasible route to personalize bone remodelling simulations.

The identification of the attractor stimulus Ψ_A from routinely available patient data may enhance the personalization of bone remodelling simulations while preserving clinical feasibility. This approach may contribute to the integration of subject-specific bone remodelling models into real medical contexts, with potential applications in both clinical decision-making and experimental research.