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

Hdl Handle:
http://hdl.handle.net/10034/620620
Title:
Parametric model of human body shape and ligaments for patient-specific epidural simulation
Authors:
Vaughan N, Dubey VN, Wee MYK, Isaacs R.
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.
Affiliation:
Bournemouth University, Poole Hospital NHS Foundation Trust
Citation:
Vaughan, N., Dubey, V. N., Wee, M. Y., & Isaacs, R. (2014). Parametric model of human body shape and ligaments for patient-specific epidural simulation. Artificial intelligence in medicine (AIIM), 62(2),129-140.
Publisher:
Elsevier
Journal:
Artificial intelligence in medicine (AIIM)
Publication Date:
1-Oct-2014
URI:
http://hdl.handle.net/10034/620620
DOI:
https://doi.org/10.1016/j.artmed.2014.08.005
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0933365714001006
Type:
Article
Language:
en
Appears in Collections:
Computer Science

Full metadata record

DC FieldValue Language
dc.contributor.authorVaughan N, Dubey VN, Wee MYK, Isaacs R.en
dc.date.accessioned2017-09-13T15:47:37Z-
dc.date.available2017-09-13T15:47:37Z-
dc.date.issued2014-10-01-
dc.identifier.citationVaughan, N., Dubey, V. N., Wee, M. Y., & Isaacs, R. (2014). Parametric model of human body shape and ligaments for patient-specific epidural simulation. Artificial intelligence in medicine (AIIM), 62(2),129-140.en
dc.identifier.doihttps://doi.org/10.1016/j.artmed.2014.08.005-
dc.identifier.urihttp://hdl.handle.net/10034/620620-
dc.description.abstractObjective 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.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0933365714001006en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectBody shapeen
dc.subjectDeformation modelen
dc.subjectPatient-specificen
dc.subjectHuman modelen
dc.subjectEpiduralen
dc.subjectSimulationen
dc.titleParametric model of human body shape and ligaments for patient-specific epidural simulationen
dc.typeArticleen
dc.contributor.departmentBournemouth University, Poole Hospital NHS Foundation Trusten
dc.identifier.journalArtificial intelligence in medicine (AIIM)en
dc.date.accepted2014-09-01-
or.grant.openaccessYesen
rioxxterms.funderunfundeden
rioxxterms.identifier.projectunfundeden
rioxxterms.versionAMen
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