A Surrogate Model Based on a Finite Element Model of Abdomen for Real-Time Visualisation of Tissue Stress during Physical Examination Training

Florence Leong, Chow Yin Lai, Siamak Farajzadeh Khosroshahi, Liang He, Simon de Lusignan, Thrishantha Nanayakkara, Mazdak Ghajari

Bioengineering, 2022

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-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 h to predict stress…

Cite as (BibTeX)

@article{leong-2022-a-surrogate-model-based,
  title={A Surrogate Model Based on a Finite Element Model of Abdomen for Real-Time Visualisation of Tissue Stress during Physical Examination Training},
  author={Florence Leong and Chow Yin Lai and Siamak Farajzadeh Khosroshahi and Liang He and Simon de Lusignan and Thrishantha Nanayakkara and Mazdak Ghajari},
  journal={Bioengineering},
  year={2022},
  doi={10.3390/bioengineering9110687}
}