Parametric model of human body shape and ligaments for patient-specific epidural simulation
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Journal: Artificial Intelligence in Medicine
Volume: 62
Issue: 2
Pages: 129-140
eISSN: 1873-2860
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2014.08.005
Abstract:Objective: This work is to build upon the concept of matching a person's weight, height and age to their overall body shape to create an adjustable three-dimensional model. A versatile and accurate predictor of body size and shape and ligament thickness is required to improve simulation for medical procedures. A model which is adjustable for any size, shape, body mass, age or height would provide ability to simulate procedures on patients of various body compositions. Methods: Three methods are provided for estimating body circumferences and ligament thicknesses for each patient. The first method is using empirical relations from body shape and size. The second method is to load a dataset from a magnetic resonance imaging (MRI) scan or ultrasound scan containing accurate ligament measurements. The third method is a developed artificial neural network (ANN) which uses MRI dataset as a training set and improves accuracy using error back-propagation, which learns to increase accuracy as more patient data is added. The ANN is trained and tested with clinical data from 23,088 patients. Results: The ANN can predict subscapular skinfold thickness within 3.54. mm, waist circumference 3.92. cm, thigh circumference 2.00. cm, arm circumference 1.21. cm, calf circumference 1.40. cm, triceps skinfold thickness 3.43. mm. Alternative regression analysis method gave overall slightly less accurate predictions for subscapular skinfold thickness within 3.75. mm, waist circumference 3.84. cm, thigh circumference 2.16. cm, arm circumference 1.34. cm, calf circumference 1.46. cm, triceps skinfold thickness 3.89. mm. These calculations are used to display a 3D graphics model of the patient's body shape using OpenGL and adjusted by 3D mesh deformations. Conclusions: A patient-specific epidural simulator is presented using the developed body shape model, able to simulate needle insertion procedures on a 3D model of any patient size and shape. The developed ANN gave the most accurate results for body shape, size and ligament thickness. The resulting simulator offers the experience of simulating needle insertions accurately whilst allowing for variation in patient body mass, height or age.
https://eprints.bournemouth.ac.uk/22759/
Source: Scopus
Parametric model of human body shape and ligaments for patient-specific epidural simulation.
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Journal: Artif Intell Med
Volume: 62
Issue: 2
Pages: 129-140
eISSN: 1873-2860
DOI: 10.1016/j.artmed.2014.08.005
Abstract:OBJECTIVE: This work is to build upon the concept of matching a person's weight, height and age to their overall body shape to create an adjustable three-dimensional model. A versatile and accurate predictor of body size and shape and ligament thickness is required to improve simulation for medical procedures. A model which is adjustable for any size, shape, body mass, age or height would provide ability to simulate procedures on patients of various body compositions. METHODS: Three methods are provided for estimating body circumferences and ligament thicknesses for each patient. The first method is using empirical relations from body shape and size. The second method is to load a dataset from a magnetic resonance imaging (MRI) scan or ultrasound scan containing accurate ligament measurements. The third method is a developed artificial neural network (ANN) which uses MRI dataset as a training set and improves accuracy using error back-propagation, which learns to increase accuracy as more patient data is added. The ANN is trained and tested with clinical data from 23,088 patients. RESULTS: The ANN can predict subscapular skinfold thickness within 3.54 mm, waist circumference 3.92 cm, thigh circumference 2.00 cm, arm circumference 1.21 cm, calf circumference 1.40 cm, triceps skinfold thickness 3.43 mm. Alternative regression analysis method gave overall slightly less accurate predictions for subscapular skinfold thickness within 3.75 mm, waist circumference 3.84 cm, thigh circumference 2.16 cm, arm circumference 1.34 cm, calf circumference 1.46 cm, triceps skinfold thickness 3.89 mm. These calculations are used to display a 3D graphics model of the patient's body shape using OpenGL and adjusted by 3D mesh deformations. CONCLUSIONS: A patient-specific epidural simulator is presented using the developed body shape model, able to simulate needle insertion procedures on a 3D model of any patient size and shape. The developed ANN gave the most accurate results for body shape, size and ligament thickness. The resulting simulator offers the experience of simulating needle insertions accurately whilst allowing for variation in patient body mass, height or age.
https://eprints.bournemouth.ac.uk/22759/
Source: PubMed
Parametric model of human body shape and ligaments for patient-specific epidural simulation
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Journal: ARTIFICIAL INTELLIGENCE IN MEDICINE
Volume: 62
Issue: 2
Pages: 129-140
eISSN: 1873-2860
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2014.08.005
https://eprints.bournemouth.ac.uk/22759/
Source: Web of Science (Lite)
Parametric model of human body shape and ligaments for patient-specific epidural simulation
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Editors: Adlassnig, K.
Journal: Artificial Intelligence in Medicine
DOI: 10.1016/j.artmed.2014.08.005
https://eprints.bournemouth.ac.uk/22759/
Source: Manual
Preferred by: Venky Dubey
Parametric model of human body shape and ligaments for patient-specific epidural simulation.
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Journal: Artif. Intell. Medicine
Volume: 62
Pages: 129-140
DOI: 10.1016/j.artmed.2014.08.005
https://eprints.bournemouth.ac.uk/22759/
Source: DBLP
Parametric model of human body shape and ligaments for patient-specific epidural simulation.
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.
Journal: Artificial intelligence in medicine
Volume: 62
Issue: 2
Pages: 129-140
eISSN: 1873-2860
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2014.08.005
Abstract:Objective
This work is to build upon the concept of matching a person's weight, height and age to their overall body shape to create an adjustable three-dimensional model. A versatile and accurate predictor of body size and shape and ligament thickness is required to improve simulation for medical procedures. A model which is adjustable for any size, shape, body mass, age or height would provide ability to simulate procedures on patients of various body compositions.Methods
Three methods are provided for estimating body circumferences and ligament thicknesses for each patient. The first method is using empirical relations from body shape and size. The second method is to load a dataset from a magnetic resonance imaging (MRI) scan or ultrasound scan containing accurate ligament measurements. The third method is a developed artificial neural network (ANN) which uses MRI dataset as a training set and improves accuracy using error back-propagation, which learns to increase accuracy as more patient data is added. The ANN is trained and tested with clinical data from 23,088 patients.Results
The ANN can predict subscapular skinfold thickness within 3.54 mm, waist circumference 3.92 cm, thigh circumference 2.00 cm, arm circumference 1.21 cm, calf circumference 1.40 cm, triceps skinfold thickness 3.43 mm. Alternative regression analysis method gave overall slightly less accurate predictions for subscapular skinfold thickness within 3.75 mm, waist circumference 3.84 cm, thigh circumference 2.16 cm, arm circumference 1.34 cm, calf circumference 1.46 cm, triceps skinfold thickness 3.89 mm. These calculations are used to display a 3D graphics model of the patient's body shape using OpenGL and adjusted by 3D mesh deformations.Conclusions
A patient-specific epidural simulator is presented using the developed body shape model, able to simulate needle insertion procedures on a 3D model of any patient size and shape. The developed ANN gave the most accurate results for body shape, size and ligament thickness. The resulting simulator offers the experience of simulating needle insertions accurately whilst allowing for variation in patient body mass, height or age.https://eprints.bournemouth.ac.uk/22759/
Source: Europe PubMed Central
Parametric model of human body shape and ligaments for patient-specific epidural simulation.
Authors: Vaughan, N., Dubey, V.N., Wee, M.Y. and Isaacs, R.
Journal: Artificial Intelligence in Medicine
Volume: 62
Issue: 2
Pages: 129-140
ISSN: 0933-3657
Abstract:This work is to build upon the concept of matching a person's weight, height and age to their overall body shape to create an adjustable three-dimensional model. A versatile and accurate predictor of body size and shape and ligament thickness is required to improve simulation for medical procedures. A model which is adjustable for any size, shape, body mass, age or height would provide ability to simulate procedures on patients of various body compositions.
https://eprints.bournemouth.ac.uk/22759/
Source: BURO EPrints