Augmented and virtual reality in dental medicine: A systematic review.

Overview of artificial intelligence-based applications in radiotherapy : Recommendations for implementation and quality assurance

Due to increasing demands in the amount of content to be learned within a medical and health sciences curriculum, there are benefits towards exploring options for new and effective delivery modes. Augmented reality technology has the potential to enhance learning in physiology and anatomy, where students require a three-dimensional knowledge of human organ systems and structures. This study aimed to assess the effectiveness of learning when an identical lesson was delivered through augmented reality using either the Microsoft HoloLens or a mobile hand-held tablet device. Thirty-eight pre-clinical undergraduate participants completed a lesson detailing the physiology and anatomy of the brain. Pre- and post-intervention tests were provided to evaluate acquired knowledge. After the activity, participants also completed a Likert-style questionnaire to evaluate adverse health effects experienced and assess their perceptions of the module. There were no significant differences between test scores from lesson delivery in either the HoloLens or mobile-based augmented reality. However, a significant increase was reported in dizziness when using the HoloLens (25% higher, n=19, p=0.04). No other adverse health effects, such as nausea, disorientation or fatigue were observed. Both modes were effective for learning, providing evidence to support educators and developers wishing to adopt an augmented reality method of delivery in health sciences and medicine.

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HoloLens and mobile augmented reality in medical and health science education: A randomised controlled trial

I1 Introduction Mona Kanaan, Noreen Dadirai Mdege, Ada Keding O1 The HiSTORIC trial: a hybrid before-and-after and stepped wedge design RA Parker, N Mills, A Shah, F Strachan, C Keerie, CJ Weir O2 Stepped wedge trials with non-uniform correlation structure Andrew Forbes, Karla Hemming O3 Challenges and solutions for the operationalisation of the ENHANCE study: a pilot stepped wedge trial within a general practice setting Sarah A Lawton, Emma Healey, Martyn Lewis, Elaine Nicholls, Clare Jinks, Valerie Tan, Andrew Finney, Christian D Mallen, on behalf of the ENHANCE Study Team O4 Early lessons from the implementation of a stepped wedge trial design investigating the effectiveness of a training intervention in busy health care settings: the Thistle study Erik Lenguerrand, Graeme MacLennan, John Norrie, Siladitya Bhattacharya, Tim Draycott, on behalf of the Thistle group O5 Sample size calculation for longitudinal cluster randomised trials: a unified framework for closed cohort and repeated cross-section designs Richard Hooper, Steven Teerenstra, Esther de Hoop, Sandra Eldridge O6 Restricted randomisation schemes for stepped-wedge studies with a cluster-level covariate Alan Girling, Monica Taljaard O7 A flexible modelling of the time trend for the analysis of stepped wedge trials: results of a simulation study Gian Luca Di Tanna, Antonio Gasparrini P1 Tackling acute kidney injury – a UK stepped wedge clinical trial of hospital-level quality improvement interventions Anna Casula, Fergus Caskey, Erik Lenguerrand, Shona Methven, Stephanie MacNeill, Margaret May, Nicholas Selby P2 Sample size considerations for quantifying secondary bacterial transmission in a stepped wedge trial of influenza vaccine Leon Danon, Hannah Christensen, Adam Finn, Margaret May P3 Sample size calculation for time-to-event data in stepped wedge cluster randomised trials Fumihito Takanashi, Ada Keding, Simon Crouch, Mona Kanaan P4 Sample size calculations for stepped-wedge cluster randomised trials with unequal cluster sizes Caroline A. Kristunas, Karen L. Smith, Laura J. Gray P5 The design of stepped wedge trials with unequal cluster sizes John N.S. Matthews P6 Promoting Recruitment using Information Management Efficiently (PRIME): a stepped wedge SWAT (study-within-a-trial) R Al-Shahi Salman, RA Parker, A Maxwell, M Dennis, A Rudd, CJ Weir P7 Implications of misspecified mixed effect models in stepped wedge trial analysis: how wrong can it be? Jennifer A Thompson, Katherine L Fielding, Calum Davey, Alexander M Aiken, James R Hargreaves, Richard J Hayes S1 Stepped Wedge Designs with Multiple Interventions Vivian H Lyons, Lingyu Li, James Hughes, Ali Rowhani-Rahbar S2 Analysis of the cross-sectional stepped wedge cluster randomised trial Karla Hemming, Monica Taljaard, Andrew Forbes

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Proceedings of the First International Conference on Stepped Wedge Trial Design

PURPOSE Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning-based approach to predict portal dosimetry based IMRT QA gamma passing rates. METHODS 182 IMRT plans for various treatment sites were planned and delivered with portal dosimetry on two TrueBeam and two Trilogy LINACs. A total of 1497 beams were collected and analyzed using gamma criteria of 2%/2 mm with a 5% threshold. The datasets for building the machine learning models consisted of 1269 beams. Ten-fold cross-validation was utilized to tune the model and prevent "overfitting." A separate test set with the remaining 228 beams was used to evaluate model performance. Each beam was characterized by a set of 31 features including both plan complexity metrics and machine characteristics. Three tree-based machine learning algorithms (AdaBoost, Random Forest, and XGBoost) were used to train the models and predict gamma passing rates. RESULTS Both AdaBoost and Random Forest had 98% of predictions within 3% of the measured 2%/2 mm gamma passing rates with a maximum error less than 4% and a mean absolute error < 1%. XGBoost showed a slightly worse prediction accuracy with 95% of the predictions within 3% of the measured gamma passing rates and a maximum error of 4.5%. The three models identified the same nine features in the top 10 most important ones that are related to plan complexity and maximum aperture displacement from the central axis or the maximum jaw size in a beam. CONCLUSION We have demonstrated that portal dosimetry IMRT QA gamma passing rates can be accurately predicted using tree-based ensemble learning models. The machine learning based approach allows physicists to better identify the failures of IMRT QA measurements and to develop proactive QA approaches.

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

Recent technological innovations have created new opportunities for the increased adoption of virtual reality (VR) and augmented reality (AR) applications in medicine While medical applications of VR have historically seen greater adoption from patient-as-user applications, the new era of VR/AR technology has created the conditions for wider adoption of clinician-as-user applications Historically, adoption to clinical use has been limited in part by the ability of the technology to achieve a sufficient quality of experience This article reviews the definitions of virtual and augmented reality and briefly covers the history of their development Currently available options for consumer-level virtual and augmented reality systems are presented, along with a discussion of technical considerations for their adoption in the clinical environment Finally, a brief review of the literature of medical VR/AR applications is presented prior to introducing a comprehensive conceptual framework for the viewing and manipulation of medical images in virtual and augmented reality Using this framework, we outline considerations for placing these methods directly into a radiology-based workflow and show how it can be applied to a variety of clinical scenarios

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Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models

The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assessment of delivery quality. The goal of this work is to investigate the potential of using treatment plan characteristics and linac performance metrics (i.e. quality control test results) in combination with machine learning techniques to predict the results of VMAT patient-specific QA measurements. Using features that describe treatment plan complexity and linac performance metrics, we trained a linear support vector classifier (SVC) to classify the results of VMAT patient-specific QA measurements. The 'targets' in this model were simple classes representing median dose difference between measured and expected dose distributions-'hot' if the median dose deviation was  >1%, 'cold' if it was  <-1%, and 'normal' if it was within  ±1%. A total of 1620 unique patient-specific QA measurements were available for model development and testing. 75% of the data were used to develop and cross-validate the model, and the remaining 25% were used for an independent assessment of model performance. For the model development phase, a recursive feature elimination (RFE) cross-validation technique was used to eliminate unimportant features. Model performance was assessed using receiver operator characteristic (ROC) curve metrics. Of the ten features found to be most predictive of patient-specific QA measurement results, half were derived from treatment plan characteristics and half from quality control (QC) metrics characterizing linac performance. The model achieved a micro-averaged area under the ROC curve of 0.93, and a macro-averaged area under the ROC curve of 0.88. This work demonstrates the potential of using both treatment plan characteristics and routine linac QC results in the development of machine learning models for VMAT patient-specific QA measurements.

https://iopscience.iop.org/article/10.1088/1361-6560/ab142e/pdf

Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics.

Introduction Caregivers of persons with dementia (PWD) frequently face physical assault and emotional abuse1,2. Providing experiential opportunities for these caregivers to develop skills that maximize safe, compassionate care is a key priority in healthcare. While simulation has been demonstrated to be more effective than didactic activities in developing clinical skills across many healthcare contexts3, in-person simulation has significant limitations. During in-person simulation, the full expression of behaviours must be limited to ensure that both learners and simulated patients (SP) are not physically harmed. While previous work in VR dementia simulations has demonstrated that taking different perspectives helps build empathy towards both the caregiver and the PWD4, VR solutions to date do not leverage a human SP, which limits the ability to explore complex behaviours. Methods The Michener Institute for Education, Baycrest Health Sciences and the Ottawa Hospital Research Institute collaborated to develop a Virtual Reality (VR) platform where simulations are delivered in a risk-free environment that fully leverages opportunities for de-escalation training, situational awareness, and empathy building. SPs enact distressed patients with dementia demonstrating fully responsive and complex behaviours that risk progressing to physical threat in real time with learner. Built into the design are opportunities for learners to take the perspective of the patient, the health care practitioner or a general observer of both. Results The objectives of this randomized controlled study are to 1) evaluate the usability and feasibility of virtual reality simulation training for clinical staff (including formal caregivers of persons with dementia, and/or those exhibiting responsive behaviours); and 2) to compare this format of training to standard training on key outcomes of interest (i.e., confidence in care; knowledge on de-escalation strategies; comfort with working with PWD). The findings will help us refine, and scale the VR training. Discussion and Conclusion This presentation will outline the objectives of the research, as well as provide a demonstration of the VR technology. The challenges of conducting this type of research during a pandemic will be discussed. References Bostrom A-M, Squires JE, Mitchell A, Sales AE, Estabrooks CA. Workplace aggression experienced by frontline staff in dementia care. Journal of Clinical Nursing. 2012 May;21(9–10):1453–65. Lachs MS, Rosen T, Teresi JA, Eimicke JP, Ramirez M, Silver S, et al. Verbal and physical aggression directed at nursing home staff by residents. J Gen Intern Med. 2013 May;28(5):660–7. Cook DA, Brydges R, Hamstra SJ, Zendejas B, Szostek JH, Wang AT, et al. Comparative effectiveness of technology-enhanced simulation versus other instructional methods: A systematic review and meta-analysis. Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 2012 Oct;7(5):308–20. Sokoloff L, Meyer R, Myers T. Virtual Reality Simulations: Building a Prototype for Dementia Education. Realities 360 Conference; 2017 Jul 26; San Jose, CA

https://stel.bmj.com/content/bmjstel/6/Suppl_1/A39.1.full.pdf

Justin Sutherland

Papers

Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models

Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics.

PG16 Virtual reality training for de-escalation of responsive behaviours