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dc.contributor.authorVaughan, Neil*
dc.contributor.authorDubey, Venketesh N.*
dc.contributor.authorWee, Michael Y. K.*
dc.contributor.authorIsaacs, Richard*
dc.date.accessioned2017-12-20T17:25:31Z
dc.date.available2017-12-20T17:25:31Z
dc.date.issued2014-09-04
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, 62(2), 129-140. https://doi.org/10.1016/j.artmed.2014.08.005en
dc.identifier.doi10.1016/j.artmed.2014.08.005
dc.identifier.urihttp://hdl.handle.net/10034/620772
dc.description.abstractObjective: This work builds 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 scan (MRI) 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 23088 patients. Results: The ANN can predict subscapular skinfold thickness within 3.54mm, waist circumference 3.92cm, thigh circumference 2.00cm, arm circumference 1.21cm, calf circumference 1.40cm, triceps skinfold thickness 3.43mm. Alternative regression analysis method gave overall slightly less accurate predictions for subscapular skinfold thickness within 3.75mm, waist circumference 3.84cm, thigh circumference 2.16cm, arm circumference 1.34cm, calf circumference 1.46cm, triceps skinfold thickness 3.89mm. 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.
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.subjectEpidural Simulationen
dc.titleParametric model of human body shape and ligaments for patient-specific epidural simulationen
dc.typeArticleen
dc.identifier.eissn1873-2860
dc.contributor.departmentBournemouth University; Poole Hospital NHS Foundation Trusten
dc.identifier.journalArtificial Intelligence in Medicine
dc.internal.reviewer-noteChecking version with author SM 20/12/17en
dc.date.accepted2014-08-10
or.grant.openaccessYesen
rioxxterms.funderPoole Hospital NHS Foundation Trusten
rioxxterms.identifier.projectInternally funded researchen
rioxxterms.versionAMen
rioxxterms.licenseref.startdate2015-09-04
html.description.abstractObjective: This work builds 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 scan (MRI) 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 23088 patients. Results: The ANN can predict subscapular skinfold thickness within 3.54mm, waist circumference 3.92cm, thigh circumference 2.00cm, arm circumference 1.21cm, calf circumference 1.40cm, triceps skinfold thickness 3.43mm. Alternative regression analysis method gave overall slightly less accurate predictions for subscapular skinfold thickness within 3.75mm, waist circumference 3.84cm, thigh circumference 2.16cm, arm circumference 1.34cm, calf circumference 1.46cm, triceps skinfold thickness 3.89mm. 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.


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