CAE Healthcare Research Articles

Evaluation of Virtual Reality High-Fidelity Adult Mannequin-based Simulation of Real-Life Clinical Scenarios in Teaching Clinical Pharmacology to Medical Students

BackgroundPresently, competency-based medical (CBME) curriculum challenges the medical educators to provide continuing education with new approaches to make learning more stimulating, motivating, and entertaining, fostering excellence in clinical practice. To address these issues, educators have advocated the use of virtual reality and mannequins to teach clinical pharmacology.ObjectivesTo study the effect, perception, and feedback of virtual reality high-fidelity adult mannequin-based (VHFM) simulation of real-life clinical scenarios over conventional tutorials in teaching clinical pharmacology to medical students.Material and MethodsAn interventional study was designed for 2nd year MBBS students for a period of 6 months. The enrolled students were randomly assigned to the test group (VHFM) or control group (tutorials). The CAE Healthcare Ltd. maestro, high-fidelity prehospital mannequin (APOLLO) was used in the test group. Three sessions consisting of six different cases were discussed, and multiple-choice questions (MCQs)-based pre-test, post–test, and retest after a month were conducted in both groups and compared. The perception and feedback of faculty and students were obtained by using a modified and revalidated questionnaire.ResultsThe test group outperformed the control group ( p <.001) in all sessions based on pre-test, post-test, and retest scores, and within-group comparisons revealed significant improvements in both groups. Students’ perceptions and feedback regarding VHFM were more enthusiastic and promising than in the tutorial group.ConclusionsVHFM is student-centered, provides an active learning environment, and aids in skill development. We strongly suggest VHFM-based learning as a complement to traditional teaching strategies in pharmacology, especially for teaching clinical reasoning to medical students.

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task

Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has been applied to analyze EEG data split into frequency bands. Although EEG is widely used in fields requiring expert performance, it has yet been used to classify surgical expertise. Thus, the goals of this study were to (a) develop an ML model to accurately differentiate skilled and less-skilled performance using EEG data recorded during a simulated surgery, (b) explore the relative importance of each EEG bandwidth to expertise, and (c) analyze differences in EEG band powers between skilled and less-skilled individuals. We hypothesized that EEG recordings during a virtual reality surgery task would accurately predict the expertise level of the participant. Twenty-one participants performed three simulated brain tumor resection procedures on the NeuroVR™ platform (CAE Healthcare, Montreal, Canada) while EEG data was recorded. Participants were divided into 2 groups. The skilled group was composed of five neurosurgeons and five senior neurosurgical residents (PGY4-6), and the less-skilled group was composed of six junior residents (PGY1-3) and five medical students. A total of 13 metrics from EEG frequency bands and ratios (e.g., alpha, theta/beta ratio) were generated. Seven ML model types were trained using EEG activity to differentiate between skilled and less-skilled groups. The artificial neural network achieved the highest testing accuracy of 100% (AUROC = 1.0). Model interpretation via Shapley analysis identified low alpha (8–10 Hz) as the most important metric for classifying expertise. Skilled surgeons displayed higher (p = 0.044) low-alpha than the less-skilled group. Furthermore, skilled surgeons displayed significantly lower TBR (p = 0.048) and significantly higher beta (13–30 Hz, p = 0.049), beta 1 (15–18 Hz, p = 0.014), and beta 2 (19–22 Hz, p = 0.015), thus establishing these metrics as important markers of expertise. ACGME Core CompetenciesPractice-Based Learning and Improvement.

Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence.

Understanding the variation of learning curves of experts and trainees for a given surgical procedure is important in implementing formative learning paradigms to accelerate mastery. The study objectives were to use artificial intelligence (AI)-derived metrics to determine the learning curves of participants in 4 groups with different expertise levels who performed a series of identical virtual reality (VR) subpial resection tasks and to identify learning curve differences among the 4 groups. A total of 50 individuals participated, 14 neurosurgeons, 4 neurosurgical fellows and 10 senior residents (seniors), 10 junior residents (juniors), and 12 medical students. All participants performed 5 repetitions of a subpial tumor resection on the NeuroVR (CAE Healthcare) platform, and 6 a priori-derived metrics selected using the K-nearest neighbors machine learning algorithm were used to assess participant learning curves. Group learning curves were plotted over the 5 trials for each metric. A mixed, repeated-measures ANOVA was performed between the first and fifth trial. For significant interactions (p < 0.05), post hoc Tukey's HSD analysis was conducted to determine the location of the significance. Overall, 5 of the 6 metrics assessed had a significant interaction (p < 0.05). The 4 groups, neurosurgeons, seniors, juniors, and medical students, showed an improvement between the first and fifth trial on at least one of the 6 metrics evaluated. Learning curves generated using AI-derived metrics provided novel insights into technical skill acquisition, based on expertise level, during repeated VR-simulated subpial tumor resections, which will allow educators to develop more focused formative educational paradigms for neurosurgical trainees.

1686 The Effect of 24-hour Call on Laparoscopic Skills of ObGyn Residents

Study Objective To assess the effect of 24-hour call on the simulated laparoscopic skills of Ob/Gyn residents from a large, tertiary-care hospital in a metropolitan city. Design A standard peg transfer drill module on the Laparoscopy VR Trainer (CAE Healthcare) was used as a surrogate for surgical skills. Participants performed the simulation module three times at the start of their call and later at the completion of call. Participants also completed a short survey of basic demographics, caffeine intake and sleep over the call shift, and prior experience on the simulator. All sessions on the simulator were logged and data, including total time, average right and left path lengths, and number of dropped pegs, was extracted. Setting n/a Patients or Participants Residents of a university-based Ob/Gyn program Interventions n/a Measurements and Main Results 22 residents were recruited for the study. A total of 35 trials were performed. Participants showed improvement in all outcomes over their call shift with a significant decrease in total time required to complete the task, a significant decrease in left and right path lengths, and significantly less pegs dropped from pre to post call sessions (p .05) for any of the outcomes. Conclusion Data revealed a statistically significant improvement in post call performances across all measured variables. This was true regardless of year in residency and was not affected by amount of sleep obtained, amount of caffeine consumed, or time since last caffeine consumption. This is one of the first studies to show an improvement after a call shift. Further studies are needed to investigate the potential impact of sleep deprivation on more complex medical procedures as well as the impact of sleep deprivation on a physician's ability to diagnose, educate, and obtain informed consent.

JUNO Jubilee: Bringing Real-Life Experiences to the Classroom!

As Program Director of the only Nuclear Medicine Technology program in Northeast Ohio, I am always looking for ways to simulate real life to the students before they enter the clinical experience at the local hospitals. Through the community bond initiative, our classroom was granted funds to acquire the Human Patient Simulator (HPS) JUNO, supplied through CAE Health Care, which was delivered late 2018.

Can You Teach Yourself Point-of-care Ultrasound to a Level of Clinical Competency? Evaluation of a Self-directed Simulation-based Training Program.

IntroductionSelf-directed learning in medical professions is established as an effective method of training in certain modalities. Furthermore, simulation technology is becoming widely used and accepted as a valid method of training for various medical skills, with ultrasound being one of the best studied. The use of point-of-care ultrasound (PoCUS) in the practice of emergency medicine is well established, and PoCUS is a core competency of the Royal College of Physicians and Surgeons of Canada emergency medicine standards. The primary goal of our study was to assess the effectiveness of a self-directed simulation-based training program for medical students, in terms of achieving competency in basic PoCUS scans.MethodsFourteen second-year medical students with no prior ultrasound experience were provided access to online study modules created by SonoSim ultrasound training solutions (SonoSim, Santa Monica, CA, US), covering ultrasound theory and methodology, and attended a two-hour introductory session where they were introduced to the study protocol, simulation equipment, and software. Participants then undertook self-directed ultrasound simulation training throughout the year, using the CAE Vimedix PoCUS simulator (CAE Healthcare, Sarasota, FL, US) and the SonoSim ultrasound training solution system. Upon reaching 10 (and 25) scans in each of the four categories (cardiac, abdomen, aorta, and pelvic), a triggered assessment was arranged in which participants scanned a live volunteer under the direct supervision of PoCUS-certified physicians. The physicians scored the participant attempts in terms of image acquisition, interpretation, and clinical understanding. No feedback was provided to the participants. Following the study, participants submitted feedback regarding the design of the study and were asked to rank their preferred training program protocols out of a provided list of five different options.ResultsAt the first triggered assessment (after completing only 10 scans in each category), four out of 14 participants were scored as competent in the aorta scan, two out of 14 participants were competent in the pelvic scan, and none of the participants were competent in both the cardiac and abdominal scans. Only nine out of 14 participants completed the second triggered assessment (after completing 25 scans in each category). At the second assessment, only three participants were scored as competent in the aorta scan, two participants were competent in the cardiac scan, and one participant was competent in the pelvic scan. None of the 14 learners completed the final phase of the training and assessment protocol. Feedback following the termination of the study showed that none of the participants supported continuing the study protocol as designed originally, and the preferred study design consisted of a full-day introductory course with live models and simulation, followed by self-directed learning with simulation and live models until 50 scans in each category were achieved.ConclusionWe were unable to demonstrate the achievement of competence in PoCUS in medical learners engaged in our combined self-directed simulation-based training program. This is in contrast to the considerable literature supporting self-directed learning and simulation-based learning for other skills. Feedback from faculty, curriculum integration, and alignment with clinical experience may be beneficial.

Acceleration of the learning curve for mastering basic critical care echocardiography using computerized simulation.

To assess the impact of computerized transthoracic echocardiography (TTE) simulation on the learning curve to achieve competency in basic critical care echocardiography (CCE). In this prospective bicenter study, noncardiologist residents novice in ultrasound followed either a previously validated training program with adjunctive computerized simulation on a mannequin (two 3h-sessions; Vimedix simulator, CAE Healthcare) (interventional group; n = 12) or solely the same training program (control group; n = 12). All trainees from the same institution were assigned to the same study group to avoid confusion bias. Each trainee was evaluated after 1 (M1), 3 (M3) and 6 (M6) months of training using our previously validated scoring system. Competency was defined by a score ≥ 90% of the maximal value. The 24 trainees performed 965 TTE in patients with cardiopulmonary compromise during their 6-month rotation. Skills assessments relied on 156 TTE performed in 106 patients (mean age 53 ± 14years; mean Simplified Acute Physiologic Score 2: 55 ± 19; 79% ventilated). When compared to the control group, trainees of the interventional group obtained a significantly higher mean skills assessment score at M1 (41.5 ± 4.9 vs. 32.3 ± 3.7: P = 0.0004) and M3 (45.8 ± 2.8 vs. 42.3 ± 3.7: P = 0.0223), but not at M6 (49.7 ± 1.2 vs. 50.0 ± 2.7: P = 0.6410), due to higher practical and technical skills scores. Trainees of the control group required significantly more supervised TTE to obtain competency than their counterparts (36 ± 7 vs. 30 ± 9: p = 0.0145). Adjunctive computerized simulation accelerates the learning curve of basic CCE in improving practical and technical skills and reduces the number of TTE examinations required to reach competency.

An empirical model for educational simulation of cervical dilation in first-stage labor

BackgroundSeveral models for educational simulation of labor and delivery were published in the literature and incorporated into a commercially available training simulator (CAE Healthcare Lucina). However, the engine of this simulator does not include a model for the clinically relevant indicators: uterine contraction amplitude and frequency, and cervical dilation. In this paper, such a model is presented for the primigravida in normal labor.MethodsThe conceptual and mathematical models represent oxytocin release by the hypothalamus, oxytocin pharmacokinetics, and oxytocin effect on uterine contractions, cervical dilation, and (positive) feedback from cervical dilation to oxytocin release by the hypothalamus.ResultsSimulation results for cervical dilation are presented, together with target data for a normal primigravida. Corresponding oxytocin concentrations and amplitude and frequency of uterine contractions are also presented.ConclusionAn original empirical model for educational simulation of oxytocin concentration, uterine contractions, and cervical dilation in first-stage labor is presented. Simulation results for cervical dilation match target data for a normal patient. The model forms a basis for taking into account more independent variables and patient profiles and can thereby considerably expand the range of training scenarios that can be simulated.

Virtual Reality Tumor Resection: The Force Pyramid Approach.

The force pyramid is a novel visual representation allowing spatial delineation of instrument force application during surgical procedures. In this study, the force pyramid concept is employed to create and quantify dominant hand, nondominant hand, and bimanual force pyramids during resection of virtual reality brain tumors. To address 4 questions: Do ergonomics and handedness influence force pyramid structure? What are the differences between dominant and nondominant force pyramids? What is the spatial distribution of forces applied in specific tumor quadrants? What differentiates "expert" and "novice" groups regarding their force pyramids? Using a simulated aspirator in the dominant hand and a simulated sucker in the nondominant hand, 6 neurosurgeons and 14 residents resected 8 different tumors using the CAE NeuroVR virtual reality neurosurgical simulation platform (CAE Healthcare, Montréal, Québec and the National Research Council Canada, Boucherville, Québec). Position and force data were used to create force pyramids and quantify tumor quadrant force distribution. Force distribution quantification demonstrates the critical role that handedness and ergonomics play on psychomotor performance during simulated brain tumor resections. Neurosurgeons concentrate their dominant hand forces in a defined crescent in the lower right tumor quadrant. Nondominant force pyramids showed a central peak force application in all groups. Bimanual force pyramids outlined the combined impact of each hand. Distinct force pyramid patterns were seen when tumor stiffness, border complexity, and color were altered. Force pyramids allow delineation of specific tumor regions requiring greater psychomotor ability to resect. This information can focus and improve resident technical skills training.

Strain Elastography &amp; Tissue Characterisation as a Tool to Differentiate Tumor from Healthy Tissue

The purpose of this study was to compare diagnostic values of normal and effected tissues with two techniques using strain elastography and tissues characterization. This study was carried out on a breast phantom containing all human body parameters. Analysis was performed using a lone phantom to correlate a relation between the values of Strain Elastography (SE) and first order texture parameters results. For SE SonixTouch Q+ (Ultrasonix Medical Corporation, 130-4311 Viking Way, Richmond, Canada) device using a linear-array ultrasound probe at a frequency of 10MHz with a gain of 40%. Elastography breast phantom was purchased from CAE healthcare USA, 3600 Edgelake Drive Sarasota FL, USA. For tissue characterization a Region of Interest (ROI) that encompasses both (normal and stiffer) areas were selected. MAZDA software was used to carry out the image analysis (mean and variance) of the tumour and healthy tissue, ROI of 1600 pixels at both regions was selected. An affirmative and resilient outcome was observed between the numerals of normal and tumor tissues, both for SE and first order texture parameters values. After our study we suggest that SE and tissue characterisation via first order texture parameter is a reliable technique to highlight normal and tumor tissue (with respect to same reference, for SE technique only). SE and first order texture parameters (mean and variance) paved way in highlighting the breast tumors fully. It is suggested that SE being more reliable approach in determining the stiffness for breast lesion, as it produces the results with real time imaging. However texture parameter gives an objective assessment of the image with a discriminating feature of the tissue.

Screening Sleep Disordered Breathing with Noncontact Measurement in a Clinical Site

[abstFig src='/00290002/06.jpg' width='300' text='Respiratory rate from simulator and Kinect' ]&lt;span class=”bold”&gt;Background and purpose:&lt;/span&gt;It has been considered that sleep-disordered breathing disorders, such as sleep apnea syndrome (SAS), cause an increase in the risk of cardiovascular disease or traffic accident risk, and thus early detection of SAS is important. It has been also important for medical workers at clinical sites to quantitatively evaluate the respiratory condition of hospitalized patients who are asleep in a simple method. A noncontact-type system was proposed to monitor the respiratory condition of sleeping patients and minimized patient-related stress such that medical workers could use the system for SAS screening and perform a preliminary check prior to definite diagnosis.&lt;span class=”bold”&gt;Method:&lt;/span&gt;The system included Microsoft Kinect™ for windows® (Kinect), a tripod, and a PC. A depth sensor of Kinect was used to measure movement in the thorax motion. Data obtained from periodic waveforms were divided with the intervals of 1 min, and the number of peaks was used to obtain the respiratory rate. Additionally, a frequency analysis was performed to calculate the respiratory frequency from a frequency at which the maximum amplitude was observed. In Experiment 1), a METI-man® PatientSimulator (CAE healthcare) (simulator) was used to study the respiratory rate and frequency calculated from the Kinect data by gradually changing the designated respiratory rate. In Experiment 2), the respiratory condition of four sleeping subjects was monitored to calculate their respiratory rate and frequencies. Furthermore, a video camera was used to confirm periodic waveforms and spectrum features of body movements during sleep.&lt;span class=”bold”&gt;Results:&lt;/span&gt;In Experiment 1), the results indicated that both the respiratory rate and frequency corresponded to the designated respiratory rate in each time zone. In Experiment 2), the results indicated that the respiratory rate of examines 1, 2, 3, and 4 corresponded to 12.79±2.44 times/min (average ± standard deviation), 16.46±4.33 times/min, 28.24±2.79 times/min, and 13.05±2.64 times/min, respectively. The findings also indicated that the frequency of examines 1, 2, 3, and 4 corresponded to 0.20±0.04 Hz, 0.26±0.06 Hz, 0.45±0.12 Hz, and 0.22±0.06 Hz, respectively. The periodic waveforms and amplitude spectra were enhanced with respect to body movements although regular waveform data were obtained after the body movement occurred.&lt;span class=”bold”&gt;Discussions:&lt;/span&gt;The results indicated that body movement and posture temporarily affected monitoring of the system. However, the findings also revealed that it was possible to calculate the respiratory rate and frequency, and thus it was considered that the system was useful for monitoring the respiration confirm with the non-contact or SAS screening of patients in clinical site.

Inherent variability in airway characteristics of simulation manikins: is it time we standardised assessments of crisis management skills?

IntroductionLearning of simulation-based crisis management skills involves technologically advanced manikins and use of automated scenarios. Progressions in preprogrammed scenarios require finite task completion such as successful airway intubations for achieving...

Integration of Echocardiography, Simulation and Traditional Teaching of Cardiovascular Physiology to First Year Medical Students

We have combined the use of echocardiography, high fidelity simulation, and team‐based learning into a traditional course in which traditional didactic processes formerly predominated, in order to advance the teaching of the cardiovascular system to first year medical students. In previous years, clicker technology was used to determine not only the ability of the students to demonstrate knowledge of physiology but also as a tool to determine gaps or deficiencies in understanding. Upon reorganization of the medical curriculum to present “thinking like a doctor” courses in the first year curriculum, we added more clinical components to the Medical Physiology course. During the cardiovascular section, echocardiography was incorporated into the Cardiac Cycle and the Heart Sounds and Murmurs lectures. In addition, high fidelity simulation was included in two of the cardiovascular laboratory sessions. The ECG laboratory included small groups of students obtaining ECGs on each other and an interactive session with larger groups of students discussing arrhythmias. Similar arrhythmias were then produced on a simulation manikin (CAE Healthcare) so that students could feel pulses and note hemodynamic changes resulting from the arrhythmias on a clinical monitor. In addition, the simulation manikin was used to present real time changes in cardiovascular parameters induced by drug administration, vagal stimulation, and occlusion of the inferior vena cava. This was followed by a computerized small group exercise in which medical students and CRNA students worked together to interpret the cardiovascular changes due to similar types of manipulations in an instrumented conscious dog (Comish K, Zucker I, MedEd PORTAL Publications; 2009). Group learning and interprofessional education was incorporated into the interpretation of mechanisms of responses to the cardiovascular manipulations. Thus we were able to successfully integrate several clinically relevant concepts to the understanding cardiovascular function by first year medical and CRNA students.

Letter to the Editor: “High-Fidelity Meets Athletic Training Education: An Innovative Collaborative Teaching Project”

Letter to the Editor: “High-Fidelity Meets Athletic Training Education: An Innovative Collaborative Teaching Project”

Modeling and simulation in biomedical engineering: applications in cardiorespiratory physiology [book reviews

Modeling and simulation have developed into indispensable disciplines in many fields including the physiological sciences; yet, there are few accessible texts available that teach the model building process in a methodical way for the physiological model developer. This text fills that need admirably. Uniquely qualified to author such a text, the author is one of the original developers of CAE Healthcare’s Human Patient Simulator (HPS), which, nearly 20 years after its release, stands alone in its physiologic and pharmacologic model sophistication for patient simulation and risk-free caregiver education. His understanding of mathematics, human physiology, pharmacology, control systems, and systems engineering, combined with a conversational writing style, results in a readable text suitable for upper-year undergraduate or early graduate courses in biomedical engineering, physiology, or applied mathematics. For those modelers or aspiring modelers reading the text, an understanding of differential equations is warranted.