Parametric model of human body shape and ligaments for patient-specific epidural simulation

This source preferred by Venky Dubey

Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.

Editors: Adlassnig, K.

http://eprints.bournemouth.ac.uk/22759/

Journal: Artificial Intelligence in Medicine

DOI: 10.1016/j.artmed.2014.08.005

This data was imported from PubMed:

Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.

http://eprints.bournemouth.ac.uk/22759/

Journal: Artif Intell Med

Volume: 62

Issue: 2

Pages: 129-140

eISSN: 1873-2860

DOI: 10.1016/j.artmed.2014.08.005

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.

This data was imported from DBLP:

Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.

http://eprints.bournemouth.ac.uk/22759/

Journal: Artificial Intelligence in Medicine

Volume: 62

Pages: 129-140

DOI: 10.1016/j.artmed.2014.08.005

This data was imported from Scopus:

Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.

http://eprints.bournemouth.ac.uk/22759/

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

© 2014 Elsevier B.V. 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.

This data was imported from Web of Science (Lite):

Authors: Vaughan, N., Dubey, V.N., Wee, M.Y.K. and Isaacs, R.

http://eprints.bournemouth.ac.uk/22759/

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

This data was imported from Europe PubMed Central:

Authors: Vaughan, N., Dubey, V.N., Wee, M.Y. and Isaacs, R.

http://eprints.bournemouth.ac.uk/22759/

Journal: Artificial intelligence in medicine

Volume: 62

Issue: 2

Pages: 129-140

eISSN: 1873-2860

ISSN: 0933-3657

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.

The data on this page was last updated at 04:42 on September 22, 2017.