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

Abstract

Objective: 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.for male or female patients aged 8-85. 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.

Educational Uses of Augmented Reality (AR): Experiences in Educational Science

“…These possibilities suggest that they can be useful objects that can be incorporated into emerging technologies such as the "flipped classroom", where the students work at home with these learning objects, and the in-person sessions are dedicated to resolving doubts and problems and conducting hands-on tasks or for the implementation of "collaborative learning" activities [94,95].…”

Modeling the behavior of human body tissues on penetration

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A review of virtual reality based training simulators for orthopaedic surgery

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