This systematic review evaluates the role of in-silico modelling in surgical devices, highlighting its significance in abdominal and thoracic surgery.
Background Research and innovation in surgery has exponentially improved over the past three decades. However, safety and efficacy assessment through randomised controlled trials require significant time and resource. In-silicomodelling may provide a solution, utilising computational simulations to develop and improve medical devices. To date, its potential in testing surgical devices is underexplored. This systematic review aims to evaluate the use of in-silico modelling for devices used in abdominal and thoracic surgery. Method PubMed databases were interrogated from 01/01/2019-01/08-2024. Inclusion criteria were defined as studies using mathematical or computational models to investigate invasive devices implanted or incorporated in the abdomen and thorax. Results The search strategy yielded 2968 studies. 42 included in the final analysis. Core themes identified included proof-of-concept (n=19), device improvement (n=22), and in-silico clinical trials (ISCTs) with simulated human patients (n=1). Specialities included: general surgery (n=4); hepato-pancreato-biliary (n=2); vascular (n=5); paediatric (n=9); and cardiothoracic (n=22). Most studies were at IDEAL stage 0 (pre-clinical) and 19 studies validated in-silico results with in-vitro or in-vivo data. Conclusion In-silico modelling has the potential to replace components of pre-clinica in-vitro and in-vivo testing of surgical devices. Applications include, the modelling of rare diseases and patient demographics, generating efficacy data for novel devices in the pre-clinical stage, and developing/repurposing existing devices to reduce known complications. A core outcome set and unified credibility framework will help standardise methodologies and reporting of results, minimising inter-study heterogeneity. Global collaboration and appropriate regulation will help bring in-silico modelling to the forefront of surgical device research.
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Pattani et al. (2025) studied this question.
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