Richard Isaacs

A review of epidural simulators: where are we today?

Thirty-one central neural blockade simulators have been implemented into clinical practice over the last thirty years either commercially or for research. This review aims to provide a detailed evaluation of why we need epidural and spinal simulators in the first instance and then draws comparisons between computer-based and manikin-based simulators. This review covers thirty-one simulators in total; sixteen of which are solely epidural simulators, nine are for epidural plus spinal or lumbar puncture simulation, and six, which are solely lumbar puncture simulators. All hardware and software components of simulators are discussed, including actuators, sensors, graphics, haptics, and virtual reality based simulators. The purpose of this comparative review is to identify the direction for future epidural simulation by outlining necessary improvements to create the ideal epidural simulator. The weaknesses of existing simulators are discussed and their strengths identified so that these can be carried forward. This review aims to provide a foundation for the future creation of advanced simulators to enhance the training of epiduralists, enabling them to comprehensively practice epidural insertion in vitro before training on patients and ultimately reducing the potential risk of harm.

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

OBJECTIVE This work is to build upon the concept of matching a persons 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 patients 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.

Haptic Interface on Measured Data for Epidural Simulation

This paper presents a haptic device with 3D computer graphics as part of a high fidelity medical epidural simulator development program. The haptic device is used as an input to move the needle in 3D, and also to generate force feedback to the user during insertion. A needle insertion trial was conducted on a porcine cadaver to obtain force data. The data generated from this trial was used to recreate the feeling of epidural insertion in the simulator. The interaction forces have been approximated to the resultant force obtained during the trial representing the force generated by the haptic device. The haptic device is interfaced with the 3D graphics for visualization. As the haptic stylus is moved, the needle moves on the screen and the depth of the needle tip indicates which tissue layer is being penetrated. Different forces are generated by the haptic device for each tissue layer as the epidural needle is inserted. As the needle enters the epidural space, the force drops to indicate loss of resistance.© 2012 ASME

Towards a realistic in vitro experience of epidural Tuohy needle insertion

The amount of pressure exerted on the syringe and the depth of needle insertion are the two key factors for successfully carrying out epidural procedure. The force feedback from the syringe plunger is helpful in judging the loss of pressure, and the depth of the needle insertion is crucial in identifying when the needle is precisely placed in the epidural space. This article presents the development of two novel wireless devices to measure these parameters to precisely guide the needle placement in the epidural space. These techniques can be directly used on patients or implemented in a simulator for improving the safety of procedure. A pilot trial has been conducted to collect depth and pressure data with the devices on a porcine cadaver. These measurements are then combined to accurately configure a haptic device for creating a realistic in vitro experience of epidural needle insertion.

Biomedical Engineering in Epidural Anaesthesia Research

The application of engineering techniques into biomedical procedures has proved extremely beneficial in many areas of medicine. A developing area is in epidural analgesia and anaesthesia, a technique employed for the relief of pain in both acute and chronic, and for anaesthesia to enable pain-free surgery. The aim of this chapter is to demonstrate several specific areas of research and how biomedical engineering techniques are used to improve and enhance the experience and training in the epidural procedure. The overall goal is to reduce the risks and subsequent morbidity in patients using advanced technologies to recreate the epidural procedure replicating as far as possible the in-vivo procedure. This would allow anaesthetists to practice the procedure in a safe and controlled environment without risk to patients. This could be achieved by recreating the sensation of the needle passing through the tissues and ligaments and by the generation of forces that match exactly those felt in-vivo. Epidural simulators are currently used as a training aid for anaesthetists, however existing simulators lack realism to various degrees and their operation is not based on measured invivo data that can accurately simulate the procedure. The techniques of advanced simulation and biomedical engineering detailed in this chapter can provide a solution.

Mechanism for Adaptive Virtual Reality Feedback

This work presents development and testing of two haptic mechanisms to simulate epidural needle insertion procedure. To configure the force feedback accuracy, we measured 20 insertions from patients in-vivo during a clinical trial. The graphics and forces adapt to the BMI of individual patients. Two haptic mechanisms were constructed: An electromagnetic haptic device (Fig. 1) and a motor driven haptic device (Fig. 2.). The resulting closed-loop system comprises manikin using four sensors and three force feedback components which can connect to our developed virtual reality epidural simulator 3D computer graphics [1]. Our literature review identified that thirty one epidural simulators have been implemented for clinical practice over the last thirty years either commercially or for research [2]. The purpose of this mechanism is to: i) connect the manikin device to computer based virtual reality graphics, ii) model insertions on various BMIs, iii) use measured data driven approach, iv) track needle orientation and depth using sensors, v) model accurate loss-of-resistance (LOR) feeling, vi) saline should escape at loss of resistance, vii) different feeling for each tissue layer, viii) mimic cerebrospinal fluid (CSF) leak on dural puncture.

Spine Flexion and Extension Model for Epidural Simulator

The aim of this research was to create novel computer graphics models of the human spine which can bend, flex and twist. The model aims to realistically duplicate the shape of the spine during various sitting positions adopted by patients during epidural anaesthesia and surgery. The extent of bending and flexing is kept within the limits of spine flexibility. Also the model vertebrate adapt in size and shape to match weight and height of specific patient bodies. The flexible spine model can be of benefit to epidural simulators which require accurate models of spinal vertebrae for needle insertion procedures.© 2014 ASME

Body Shape and Size Modelling Using Regression Analysis and Neural Network Prediction

The aim of this research is to build a patient-specific virtual body shape model for patients of various Body Mass Index (BMI) and body shape. This will enable simulated epidural procedure on patients of various body characteristics, to increase trainee skill, reduce injuries and litigation costs.Regression analysis (RA) and artificial neural networks (ANN) were implemented to accurately calculate body shape in a data-driven approach. Epidural simulator software was developed containing a screen to enter patient characteristics. When the patient BMI is adjusted, the modelled body shape and tissue layer thickness updates allowing patient specific simulation. The model uses anthropometric measurements as input: body mass, height, age, gender and body shape.The developed model enables a virtual representation of any actual patient to be built based on their measured parameters for epidural rehearsal prior to in-vivo procedure.Copyright

In-Vivo Obstetric Pressure Measurements for Patient-Specific Epidural Simulator

The aim of this study was to measure changing pressures during Tuohy epidural needle insertions for obstetric parturients of various BMI. This has identified correlations between BMI and epidural pressure. Also we investigated links between BMI and the thicknesses and depths of ligaments and epidural space as measured from MRI and ultrasound scans. To date there have been no studies relating epidural pressure and ligament thickness changes with varying Body Mass Indices (BMI).Further goals following measurement of pressure differences between various BMI patients, were to allow a patient-specific epidural simulator to be developed, which has not been achieved before. The trial has also assessed the suitability of our in-house developed wireless pressure measurement device for use in-vivo. Previously we conducted needle insertion trial with porcine for validation of the measurement system.Results showed that for each group average pressures during insertion decrease as BMI increases. Pressure measurements obtained from the patients were matched to tissue thickness measurements from MRI and ultrasound scans. The mean Loss of Resistance (LOR) pressure in each group reduces as BMI increases. Variation in the shape of the pressure graphs was noticed between two epiduralists performing the procedure, suggesting each anaesthetist may have a signature graph shape. This is a new finding which offers potential use in epidural training and assessment. It can be seen that insertions performed by the first epiduralist have a higher pressure range than insertions performed by second epiduralist.Copyright

Artificial Neural Network to Predict Patient Body Circumferences and Ligament Thicknesses

An artificial neural network has been implemented and trained with clinical data from 23088 patients. The aim was to predict a patient’s body circumferences and ligament thickness from patient data. A fully connected feed-forward neural network is used, containing no loops and one hidden layer and the learning mechanism is back-propagation of error. Neural network inputs were mass, height, age and gender. There are eight hidden neurons and one output. The network can generate estimates for waist, arm, calf and thigh circumferences and thickness of skin, fat, Supraspinous and interspinous ligaments, ligamentum flavum and epidural space. Data was divided into a training set of 11000 patients and an unseen test data set of 12088 patients. Twenty five training cycles were completed. After each training cycle neuron outputs advanced closer to the clinically measured data. Waist circumference was predicted within 3.92cm (3.10% error), thigh circumference 2.00cm, (2.81% error), arm circumference 1.21cm (2.48% error), calf circumference 1.41cm, (3.40% error), triceps skinfold 3.43mm, (7.80% error), subscapular skinfold 3.54mm, (8.46% error) and BMI was estimated within 0.46 (0.69% error). The neural network has been extended to predict ligament thicknesses using data from MRI. These predictions will then be used to configure a simulator to offer a patient-specific training experience.Copyright