About this Research Topic
In the last decades, literature provided a wide amount of examples of in-silico studies investigating different aspects of biomedical devices.
However, since these models are usually characterized by a progressively increasing level of complexity and realism, it is necessary to pursue a systematic verification and validation phase to ensure their accuracy for the intended use.
The ASME V&V 40 subcommittee has developed a framework to guide how to establish and communicate risk-informed credibility of computational models for medical device performance assessment, referring also to V&V10 and 20. In particular, the Standard addresses the credibility of the model to a specific Context Of Use (COU), i.e. the specific role and purpose of the model used for its predictive capabilities to answer a Question Of Interest (QOI). The risks associated with the model, on the other hand, arise from the possibility that incorrect decisions and undesirable results, such as patient injury or device malfunction, may result from the use of the model. The model risk is a combination of the influence of the computational model with other evidence to make a decision and a consequence of the COU credibility activities.
The credibility assessment of a medical device computational model is strictly related with its verification and validation. In particular, the validation phase is non-trivial and requires procedures that face the intrinsic difficulties of an experimental campaign over a complex system to generate in-vitro and/or in-vivo data on which to base the comparison with the computational outputs. Moreover, when setting up an in-silico approach, there is an intrinsic dualism that balances, on one hand, the desire of a high degree of realism and, on the other hand, its usability.
In the available guidelines and in the literature, only qualitative indications and few applicative examples have been provided about the procedure to be followed for verifying and validating in silico models of medical devices, and hence performing risk-informed credibility assessment.
Clearly, it would be very useful for the scientific community as well as for biomedical companies if, starting from guidelines, for each device, it would be defined an ad hoc protocol for validating the computational model and clearly show how to implement it in light of the intended use and the possible impact of the model.
Referring in particular to implantable devices, the goal of this Research Topic is to organize a sort of manual where for many different devices (cardiovascular, orthopedic, neurological, etc.), different contexts of use and questions of interest, it is defined and shown step by step the path to be followed for obtaining a risk-informed credibility assessment.
We would like to acknowledge Dr. Georgia Karanasiou, who has acted as coordinator and has contributed to the preparation of the proposal for this Research Topic.
However, since these models are usually characterized by a progressively increasing level of complexity and realism, it is necessary to pursue a systematic verification and validation phase to ensure their accuracy for the intended use.
The ASME V&V 40 subcommittee has developed a framework to guide how to establish and communicate risk-informed credibility of computational models for medical device performance assessment, referring also to V&V10 and 20. In particular, the Standard addresses the credibility of the model to a specific Context Of Use (COU), i.e. the specific role and purpose of the model used for its predictive capabilities to answer a Question Of Interest (QOI). The risks associated with the model, on the other hand, arise from the possibility that incorrect decisions and undesirable results, such as patient injury or device malfunction, may result from the use of the model. The model risk is a combination of the influence of the computational model with other evidence to make a decision and a consequence of the COU credibility activities.
The credibility assessment of a medical device computational model is strictly related with its verification and validation. In particular, the validation phase is non-trivial and requires procedures that face the intrinsic difficulties of an experimental campaign over a complex system to generate in-vitro and/or in-vivo data on which to base the comparison with the computational outputs. Moreover, when setting up an in-silico approach, there is an intrinsic dualism that balances, on one hand, the desire of a high degree of realism and, on the other hand, its usability.
In the available guidelines and in the literature, only qualitative indications and few applicative examples have been provided about the procedure to be followed for verifying and validating in silico models of medical devices, and hence performing risk-informed credibility assessment.
Clearly, it would be very useful for the scientific community as well as for biomedical companies if, starting from guidelines, for each device, it would be defined an ad hoc protocol for validating the computational model and clearly show how to implement it in light of the intended use and the possible impact of the model.
Referring in particular to implantable devices, the goal of this Research Topic is to organize a sort of manual where for many different devices (cardiovascular, orthopedic, neurological, etc.), different contexts of use and questions of interest, it is defined and shown step by step the path to be followed for obtaining a risk-informed credibility assessment.
We would like to acknowledge Dr. Georgia Karanasiou, who has acted as coordinator and has contributed to the preparation of the proposal for this Research Topic.
Keywords: credibility, validation, computational models, biomedical model prediction
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