Credibility assessment of computational models according to ASME V&V40: Application to the Bologna Biomechanical Computed Tomography solution.

BACKGROUND AND OBJECTIVE

When a computational model aims to be adopted beyond research purposes, e.g. to inform a clinical or regulatory decision, trust must be placed in its predictive accuracy. This practically translates into the need to demonstrate its credibility. In fact, prior to its adoption for regulatory purposes, an in silico methodology should be proven credible enough for the scope. This has become especially relevant as, although evidence of the safety and efficacy of new medical products or interventions has been traditionally provided to the regulator experimentally, i.e., in vivo or ex vivo, recently the idea to inform a regulatory decision in silico has made its way in the regulatory scenario. While a harmonised technical standard is currently missing in the EU regulatory system, in 2018 the ASME issued V&V40-2018, where a risk-based framework to assess the credibility of a computational model through the performance of predefined credibility activities is provided. The credibility framework is here applied to Bologna Biomechanical Computed Tomography (BBCT) solution, which predicts the absolute risk of fracture at the femur for a subject. BBCT has recently been the object of a qualification advice request to the European Medicine Agency.

METHODS

The full implementation of ASME V&V40-2018 framework on BBCT is shown. Starting from BBCT proposed context of use the whole credibility plan is presented and the credibility activities (Verification, Validation, Applicability) described together with the achieved credibility levels.

RESULTS

BBCT risk is judged medium, and the credibility levels achieved considered acceptable. The uncertainties intrinsically present in the material properties assignment affected BBCT predictions to the highest extent.

CONCLUSIONS

This work provides the practical application of the ASME V&V40-2018 risk-based credibility assessment framework, which could be applied to demonstrate model credibility in any field and support future regulatory submissions and foster the adoption of In Silico Trials.