An overview of augmented and virtual reality applications in radiotherapy and future developments enabled by modern tablet devices

2013 ◽  
Vol 13 (3) ◽  
pp. 350-364 ◽  
Author(s):  
F. Cosentino ◽  
N. W. John ◽  
J. Vaarkamp
Keyword(s):  
Virtual Reality ◽  
Clinical Decision Making ◽  
Image Data ◽  
Clinical Decision ◽  
Small Mass ◽  
Full Potential ◽  
Tablet Devices ◽  
Team Meetings ◽  
Future Developments ◽  
3D Effect

AbstractPurposeWe review augmented (AR) and virtual reality (VR) applications in radiotherapy as found in the scientific literature and highlight future developments enabled by the use of small mass-produced devices and portability of techniques developed in other fields to radiotherapy.AnalysisThe application of AR and VR within radiotherapy is still in its infancy, with the notable exception of training and teaching applications. The relatively high cost of equipment needed to generate a realistic 3D effect seems one factor that has slowed down its use, but also the sheer amount of image data is relatively recent, were radiotherapy professionals are only beginning to explore how to use this to its full potential. This increased availability of 3D data in radiotherapy will drive the application of AR and VR in radiotherapy to efficiently recognise and extract key features in the data to act on in clinical decision making.ConclusionThe development of small mass-produced tablet devices coming on the market will allow the user to interact with computer-generated information more easily, facilitating the application of AR and VR. The increased connectivity enabling virtual presence of remote multidisciplinary team meetings heralds significant changes to how radiotherapy professionals will work, to the benefit of our patients.

scholarly journals P1417 Acceptability of a virtual reality system for examination of congenital heart disease patients

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
G Wheeler ◽  
S Deng ◽  
K Pushparajah ◽  
J A Schnabel ◽  
J M Simpson ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Virtual Reality ◽  
Heart Disease ◽  
Congenital Heart ◽  
Clinical Decision Making ◽  
Training Session ◽  
Image Data ◽  
Clinical Decision ◽  
Anatomical Landmarks ◽  
Brief Training

Abstract Funding Acknowledgements Work supported by the NIHR i4i funded 3D Heart project [II-LA-0716-20001] Background/Introduction Virtual Reality (VR) has recently gained great interest for examining 3D images from congenital heart disease (CHD) patients. Currently, 3D printed models of the heart may be used for particularly complex cases. These have been found to be intuitive and to positively impact clinical decision-making. Although positively received, such printed models must be segmented from the image data, generally only CT/MR may be used, the prints are static, and models do not allow for cropping / slicing or easy manipulation. Our VR system is designed to address these issues, as well as providing a simple interface compared to standard software. Building such a VR system, one with intuitive interaction which is clinically useful, requires studying user acceptance and requirements. Purpose: We evaluate the usability of our VR system can a prototype VR system be easily learned and used by clinicians unfamiliar with VR. Method We tested a VR system which can display 3D echo images and enables the user to interact with them, for instance by translating, rotating and cropping. Our system is tested on a transoesophageal echocardiogram from a patient with aortic valve disease. 13 clinicians evaluated the system including 5 imaging cardiologists, 5 physiologists, 2 surgeons and an interventionist, with their clinical experience ranging from trainee to more than 5 years’ of experience. None had used VR regularly in the past. After a brief training session, they were asked to place three anatomical landmarks and identify a particular cardiac view. They then completed a questionnaire on system ease of learning and image manipulation. Results: Results are shown in the figure below. Learning to use the system was perceived as easy for all but one participant, who rated it as ‘Somewhat difficult’. However, once trained, all users found the system easy to use. Participants found the interaction, where objects in the scene are picked up using the controller and then track the controller’s motion in a 1:1 way, to be particularly easy to learn and use. Conclusion Our VR system was accepted by the vast majority of clinicians, both for ease of learning and use. Intuitiveness and the ability to interact with images in a natural way were highlighted as most useful - suggesting that such a system could become accepted for routine clinical use in the future. Abstract P1417 Figure. VR system evaluation participant feedbac

scholarly journals Development and Effects of a Nursing Education Program Using Virtual Reality for Enhancing Clinical Decision-Making Ability in Respiratory Disease Nursing Care

2021 ◽  
Vol 28 (4) ◽  
pp. 458-469
Author(s):  
Eun Ju Lee ◽  
Min Jung Ryu
Keyword(s):  
Decision Making ◽  
Virtual Reality ◽  
Nursing Education ◽  
Respiratory Disease ◽  
Nursing Care ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Control Group ◽  
Simulation Education ◽  
Experimental Group

Purpose: This study was conducted to develop and examine the effects of a nursing education program using virtual reality to enhance clinical decision-making ability in respiratory disease nursing care by assessing students’ confidence in performance, clinical decision-making ability, practice flow, class evaluations, and simulation design evaluations.Methods: This study was developed based on the Jeffries simulation model and 5E learning cycle model, blending a virtual reality simulation and high-fidelity simulation. The participants were 41 third-year nursing students with no virtual reality and simulation education experience. The experimental group (n=21) received the virtual reality program, while the control group (n=20) received traditional simulation education. Data were collected from March 8 to May 28, 2021 and analyzed using SPSS version 27 for Windows.Results: Statistically significant differences were found between the experimental group and the control group post-intervention in confidence in performance (F=4.88, p=.33) and clinical decision-making ability (F=18.68, p＜.001). The experimental group showed significant increases in practice flow (t=2.34, p=.024) and class evaluations (t=2.99, p=.005) compared to the control group.Conclusion: Nursing education programs using virtual reality to enhance clinical decision-making ability in respiratory disease nursing care can be an effective educational strategy in the clinical context.

Use of the functional independence measure in Japanese rehabilitation team interaction

2019 ◽  
Vol 21 (6) ◽  
pp. 660-689
Author(s):  
Hiroaki Izumi
Keyword(s):  
Clinical Decision Making ◽  
Collaborative Work ◽  
Functional Independence Measure ◽  
Clinical Decision ◽  
Functional Independence ◽  
Disabled Persons ◽  
Rehabilitation Team ◽  
Team Members ◽  
Team Interaction ◽  
Team Meetings

The functional independence measure (FIM) is a clinical scale which is used to evaluate the amount of assistance disabled persons need to conduct their daily living activities. Drawing on 65 video-recorded rehabilitation team meetings and medical records collected from a Japanese hospital, this article utilizes ethnomethodology and conversation analysis to uncover how Japanese rehabilitation team members use the FIM to track changes in the functional status of patients and decide the length of stay in ongoing interactional sequences. Analysis shows that a series of the FIM scores assembled and arranged in situ provide a sequential framework for members to understand the progress of rehabilitation and predict the plateau phase. Moreover, a particular expert is asked about patients’ capacity for further improvements and his or her opinions are treated as a basis for clinical decisions. In this way, diagnostic and clinical decision-making is produced through the ongoing collaborative work of various specialists.

scholarly journals Evidence-Based Practices and Outcomes for Children with Mild and Unilateral Hearing Loss

2020 ◽  
Vol 51 (1) ◽  
pp. 1-4
Author(s):  
Elizabeth A. Walker
Keyword(s):  
Hearing Loss ◽  
Cognitive Abilities ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Unilateral Hearing Loss ◽  
Full Potential ◽  
Listening Effort ◽  
Outcomes For Children ◽  
Children With Hearing Loss ◽  
The Impact

Purpose This forum provides an overview of current research and clinical practice for children with mild bilateral or unilateral hearing loss. Historically, there has been ambiguity surrounding the need for intervention in this population. Our goal is to explore the literature on outcomes and treatment so that audiologists, speech-language pathologists, teachers, physicians, and families can be confident in the clinical decision-making process when working with these children. To that end, topics include (a) progression of mild hearing loss in children; (b) the impact of mild or unilateral hearing loss on language, listening, and cognitive abilities; (c) research and reviews on intervention approaches; and (d) listening effort and fatigue in unilateral hearing loss. Conclusion Uncertainty about outcomes and treatment approaches for children with mild or unilateral hearing loss leads to inconsistent intervention and increased developmental risk. We hope that this forum will generate productive discussion among researchers and clinicians to ensure that all children with hearing loss reach their full potential.

scholarly journals Cancer Multidisciplinary Team Meetings: Evidence, Challenges, and the Role of Clinical Decision Support Technology

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Vivek Patkar ◽  
Dionisio Acosta ◽  
Tim Davidson ◽  
Alison Jones ◽  
John Fox ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Multidisciplinary Team ◽  
Care Delivery ◽  
Clinical Decision ◽  
Full Potential ◽  
Team Meetings ◽  
The Status ◽  
The Impact

Multidisciplinary team (MDT) model in cancer care was introduced and endorsed to ensure that care delivery is consistent with the best available evidence. Over the last few years, regular MDT meetings have become a standard practice in oncology and gained the status of the key decision-making forum for patient management. Despite the fact that cancer MDT meetings are well accepted by clinicians, concerns are raised over the paucity of good-quality evidence on their overall impact. There are also concerns over lack of the appropriate support for this important but overburdened decision-making platform. The growing acceptance by clinical community of the health information technology in recent years has created new opportunities and possibilities of using advanced clinical decision support (CDS) systems to realise full potential of cancer MDT meetings. In this paper, we present targeted summary of the available evidence on the impact of cancer MDT meetings, discuss the reported challenges, and explore the role that a CDS technology could play in addressing some of these challenges.

scholarly journals A Clinical Decision-Making Framework for the Use of Video Gaming as a Therapeutic Modality

2021 ◽  
Vol 12 ◽  
Author(s):  
Debbie Espy ◽  
Ann Reinthal ◽  
Vanina Dal Bello-Haas
Keyword(s):  
Decision Making ◽  
Virtual Reality ◽  
Motor Learning ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Task Demands ◽  
Therapeutic Modality ◽  
Video Gaming ◽  
Therapeutic Exercise ◽  
Therapeutic Goals

Virtual reality and video gaming offer modulation of more exercise and motor learning parameters simultaneously than other modalities; however, there is a demonstrated need for resources to facilitate their effective use clinically. This article presents a conceptual framework to guide clinical-decision making for the selection, adaptation, modulation, and progression of virtual reality or gaming when used as a therapeutic exercise modality, and two cases as exemplars. This framework was developed by adapting the steps of theory derivation, whereby concepts and parent theories are brought together to describe a new structure or phenomenon of interest. Specifically, motor learning theory, integrated motor control theory, Gentile's Taxonomy of Tasks, and therapeutic exercise principles were integrated to develop this framework. It incorporates person (body segment), environmental, and task demands; each demand is comprised of realm, category, choice, and continuum parameters as motor training considerations and alternatives for decision-making. This framework: (1) provides structure to guide clinical decisions for effective and safe use of virtual reality or gaming to meet therapeutic goals and requirements, (2) is a concise and organized method to identify, document, and track the therapeutic components of protocols and client progression over time; (3) can facilitate documentation for reimbursement and communication among clinicians; and, (4) structures student learning, and (5) informs research questions and methods.

scholarly journals Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 382 ◽  
Author(s):  
Sen Liang ◽  
Rongguo Zhang ◽  
Dayang Liang ◽  
Tianci Song ◽  
Tao Ai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Decision Making ◽  
Three Dimensional ◽  
Area Under The Curve ◽  
Image Data ◽  
Clinical Decision ◽  
The Cancer Genome Atlas ◽  
World Health ◽  
Validation Dataset ◽  
Who Grade

Non-invasive prediction of isocitrate dehydrogenase (IDH) genotype plays an important role in tumor glioma diagnosis and prognosis. Recently, research has shown that radiology images can be a potential tool for genotype prediction, and fusion of multi-modality data by deep learning methods can further provide complementary information to enhance prediction accuracy. However, it still does not have an effective deep learning architecture to predict IDH genotype with three-dimensional (3D) multimodal medical images. In this paper, we proposed a novel multimodal 3D DenseNet (M3D-DenseNet) model to predict IDH genotypes with multimodal magnetic resonance imaging (MRI) data. To evaluate its performance, we conducted experiments on the BRATS-2017 and The Cancer Genome Atlas breast invasive carcinoma (TCGA-BRCA) dataset to get image data as input and gene mutation information as the target, respectively. We achieved 84.6% accuracy (area under the curve (AUC) = 85.7%) on the validation dataset. To evaluate its generalizability, we applied transfer learning techniques to predict World Health Organization (WHO) grade status, which also achieved a high accuracy of 91.4% (AUC = 94.8%) on validation dataset. With the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multimodal radiogenomics problems and has the potential to be applied in clinical decision making.

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