2022
DOI: 10.3390/bioengineering9110687
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A Surrogate Model Based on a Finite Element Model of Abdomen for Real-Time Visualisation of Tissue Stress during Physical Examination Training

Abstract: Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real… Show more

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Cited by 4 publications
(2 citation statements)
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“…Possible use cases encompass, for example, studies on 1) the effect of individual braces in scoliosis treatment (Périé et al, 2004;Clin et al, 2010;Sattout et al, 2016), or of lumbar orthoses (Molimard et al, 2019;Bonnaire et al, 2020) on the lumbosacral spine, 2) injury prevention in crash testing (King, 2018;Untaroiu et al, 2018;Grébonval et al, 2021) and stiff structure impact (Lee and Yang, 2001;Haug et al, 2004;Snedeker et al, 2007) or vertical impact load (Cox, 2020) studies, or 3) the load removal of the spine by increasing the intra-abdominal pressure (El-Monajjed and Driscoll, 2020;Guo et al, 2021). Another use case is modelling the interaction of organs (Misra et al, 2008), or the abdominal wall with surgical instruments (Hernández et al, 2011); these models are used for virtual surgical planning or support of education (Leong et al, 2022). Recently, authors analyzed the effects of muscular contractions on the biomechanics of the abdominal wall numerically (Pavan et al, 2019;Todros et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…Possible use cases encompass, for example, studies on 1) the effect of individual braces in scoliosis treatment (Périé et al, 2004;Clin et al, 2010;Sattout et al, 2016), or of lumbar orthoses (Molimard et al, 2019;Bonnaire et al, 2020) on the lumbosacral spine, 2) injury prevention in crash testing (King, 2018;Untaroiu et al, 2018;Grébonval et al, 2021) and stiff structure impact (Lee and Yang, 2001;Haug et al, 2004;Snedeker et al, 2007) or vertical impact load (Cox, 2020) studies, or 3) the load removal of the spine by increasing the intra-abdominal pressure (El-Monajjed and Driscoll, 2020;Guo et al, 2021). Another use case is modelling the interaction of organs (Misra et al, 2008), or the abdominal wall with surgical instruments (Hernández et al, 2011); these models are used for virtual surgical planning or support of education (Leong et al, 2022). Recently, authors analyzed the effects of muscular contractions on the biomechanics of the abdominal wall numerically (Pavan et al, 2019;Todros et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…Recently, a machine learning ap-proach that trains supervised artificial neural networks (ANNs) with data obtained from FEM has been proven efficient in generating surrogate models that can predict the deformation of SPAs accurately. [14][15][16] Liu et al 17 used it for rapid evaluation in the design optimisation of soft grippers. Despite the benefits of this approach, only a small design space of the actuator has been explored, making it inefficient in FEM data collection and shape matching.…”
Section: Introductionmentioning
confidence: 99%