Artif Intell Med - Parametric model of human body shape and ligaments for patient-specific epidural simulation.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv work build upon concept match person weight height age overal bodi shape creat adjust threedimension model versatil accur predictor bodi size shape ligament thick requir improv simul medic procedur model adjust size shape bodi mass age height provid abil simul procedur patient various bodi compositionsmethod three method provid estim bodi circumfer ligament thick patient first method use empir relat bodi shape size second method load dataset magnet reson imag mri scan ultrasound scan contain accur ligament measur third method develop artifici neural network ann use mri dataset train set improv accuraci use error backpropag learn increas accuraci patient data ad ann train test clinic data patientsresult ann can predict subscapular skinfold thick within mm waist circumfer cm thigh circumfer cm arm circumfer cm calf circumfer cm tricep skinfold thick mm altern regress analysi method gave overal slight less accur predict subscapular skinfold thick within mm waist circumfer cm thigh circumfer cm arm circumfer cm calf circumfer cm tricep skinfold thick mm calcul use display d graphic model patient bodi shape use opengl adjust d mesh deformationsconclus patientspecif epidur simul present use develop bodi shape model abl simul needl insert procedur d model patient size shape develop ann gave accur result bodi shape size ligament thick result simul offer experi simul needl insert accur whilst allow variat patient bodi mass height age

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