Imperial College London
Alternate

Dr Mazdak Ghajari

Faculty of Engineering, Dyson School of Design Engineering

Reader in Brain Biomechanics
 
 
 
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Contact

 

+44 (0)20 7594 9236m.ghajari Website

 
 
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Location

 

Dyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Leong:2022:10.3390/bioengineering9110687,
author = {Leong, F and Chow, Yin L and Siamak, Farajzadeh K and He, L and Simon, DL and Thrishantha, N and mazdak, G},
doi = {10.3390/bioengineering9110687},
journal = {Bioengineering},
title = {A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training},
url = {http://dx.doi.org/10.3390/bioengineering9110687},
volume = {9},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Robotic patients show great potential to improve medical palpation training as they can provide feedback that cannot be obtained in a real patient. Providing information about internal organs deformation can significantly enhance palpation training by giving medical trainees visual insight based on their finger behaviours. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, thus able to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics 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 (ANN) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 hrs to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that the ANN has a 92.6% accuracy. We also show that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has potential to be used as a training simulator for trainees to hone their palpation skills.
AU  - Leong,F
AU  - Chow,Yin L
AU  - Siamak,Farajzadeh K
AU  - He,L
AU  - Simon,DL
AU  - Thrishantha,N
AU  - mazdak,G
DO  - 10.3390/bioengineering9110687
PY  - 2022///
SN  - 2306-5354
TI  - A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training
T2  - Bioengineering
UR  - http://dx.doi.org/10.3390/bioengineering9110687
UR  - http://hdl.handle.net/10044/1/101441
VL  - 9
ER  -
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