A Systematic Literature Review of Practical Virtual and Augmented Reality Solutions in Surgery

Roessel, Jonas; Knoell, Moritz; Hofmann, Jannic; Buettner, Ricardo

doi:10.1109/compsac48688.2020.0-204

Cited by 14 publications

(11 citation statements)

References 79 publications

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“…Additionally, it is used in the psychological relief and treatment of the individual such as phobia treatment, anxiety disorder treatment, pain and anxiety reduction (Ahmadpour et al, 2019; Garrett et al, 2018; Mishkind et al, 2017; Mitrousia & Giotakos, 2016; Park et al, 2019). It is predicted that using VR in the healthcare industry will have a more important role in the following years (Roessel et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Effect of Showing Images of the Foetus with the Virtual Reality Glass During Labour Process on Labour Pain, Birth Perception and Anxiety

Akın

KOÇAK

Kucukaydin

et al. 2021

Journal of Clinical Nursing

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Aim and objectives This study was conducted to determine the effect of showing images of the foetus to the pregnant women with the virtual reality glass during labour process on labour pain, childbirth perception and anxiety level. Background Virtual reality is an effective and inexpensive method that allows the creation of simulated scenarios in which it interacts with the virtual environment with multisensory stimuli. Design This is a randomised controlled experimental study. This study was conducted with the guidelines of Consolidated Standards of Reporting Trials (CONSORT). The study was enrolled in the Clinical Trials database (NCT04664114). Methods The study included 100 pregnant women (50 intervention group and 50 control group). Ultrasound images of the foetus were recorded on the 28th week of pregnancy of the women in the intervention group. These images were shown to the women with the virtual reality glass during labour process. Routine procedures were carried out for the women in the control group. The Visual Analog Scale (VAS) was applied to both groups when cervical dilatation was 4 cm and 9 cm. The Women's Perception for the Scale of Supportive Care Given During Labor (POBS) and the Perinatal Anxiety Screening Scale (PASS) were applied almost two hours after labour. Results The VAS scores of the women in the intervention group with 9 cm dilatation and their PASS mean scores were significantly lower than women in the control group while their POBS score were significantly higher (p < 0.01). Conclusion The results of this study show that showing images of the foetus to women with virtual reality decreased labour pain and anxiety level. Relevance to clinical practice It is known that supportive care given during labour is important both for the mother's and baby's health. Therefore, it is important that nurses and midwives, who spend the most time with women and provide the most support, reduce the pain and anxiety of the woman with nonpharmacological practices and make them feel positive feelings about delivery.

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Section: Introductionmentioning

confidence: 99%

The Effect of Showing Images of the Foetus with the Virtual Reality Glass During Labour Process on Labour Pain, Birth Perception and Anxiety

Akın

KOÇAK

Kucukaydin

et al. 2021

Journal of Clinical Nursing

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“…Studies VR [6], [5], [7], [12], [13], [15], [17], [18], [20], [21], [22], [23], [26], [27], [30], [31], [32], [33], [34], [36], [35], [37], [38], [41], [42], [43], [47], [49], [50], [54], [57], [59], [60], [61] EDA [2], [3], [8], [9], [11], [12], [16], [20], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34...…”

Section: Table V Primary Studies Applied As Input Conceptmentioning

confidence: 99%

“…Instead of human senses being optimal for VR, haptics and VR technology are developed and adapted to human senses sensors (hearing, vision, and touch) that may be applied and promoted for individual engagement through simulation, enabling the framework to identify and assist MCI symptoms. Individuals naturally prefer a multimodal approach for a higher level of interaction [31], [57], [67], [83]. Yet, challenges include i) cost, ii) storage of data, iii) participant sample size and d) requirements of users, as identified in the following studies [9], [12], [13], [23], [24], [26], [28], [29], [30], [33], [37], [38].…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality and Electrodermal Activity to Support Mild Cognitive Impairment: A Systematic Literature Review

Patient¹

2022

Preprint

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<p>This review explores mild cognitive impairment support, focusing on virtual reality (VR) applications coupled with electrodermal activity (EDA). A systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement were performed from an original inquiry of 698 articles from 11 digital libraries. An independent review of selected studies assessed the risk of bias and obtained data. The meta-analysis included 44 studies, with a total number of 9099 participants in all the papers reviewed were split into (5200) cognitive or (2304) non-cognitive impairments. The proportion of cognitive versus non-cognitive had a first risk difference (RD = 0.32; p-value = 0.0001). The comparison of female to male participants showed (OR = 6.10; p-value = 0.00001). Anxiety, depression, and stress conditions had the effect of (SMD = -0.39; p-value = 0.09). In the analysis, the general lack of research restricts virtual reality and electrodermal activity to sustain mild cognitive impairments. Forty-five excluded papers applied data from pre-2017. Two studies with 46 participants had a result of (SMD = 0.60; p-value = 0.31). Identifying VR and EDA-based studies investigating training and rehabilitation to support MCI symptoms in individuals.</p>

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“…CNN are one of the deep learning methods, which is a sub-branch of machine learning and has the ability to learn through examples. CNN can learn from raw image or text data and can increase prediction accuracy according to the size of the data [6,35,37,44,45,[52][53][54][55]. In this work, detection and classification of pressure injuries will be accomplished with a deep learning model.…”

Section: Deep Learning Model In Detection and Classification Of Pressure Injuriesmentioning

confidence: 99%

Prediction and classification of pressure injuries by deep learning

Yilmaz¹,

Kızıl²,

Kaya³

et al. 2021

hpc

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Authors' contributionWkład autorów: A. Study design/planning zaplanowanie badań B. Data collection/entry zebranie danych C. Data analysis/statistics dane -analiza i statystyki D. Data interpretation interpretacja danych E. Preparation of manuscript przygotowanie artykułu F. Literature analysis/search wyszukiwanie i analiza literatury G. Funds collection zebranie funduszy SummaryPressure injuries are a serious medical problem that both negatively affects the patient's quality of life and results in significant healthcare costs. In cases where a patient doesn't receive appropriate treatment and care, death may result. Nurses play critical roles in the prevention, care, and treatment of pressure injuries as members of the healthcare team who closely monitor the health status of the patient. Today, the use of artificial intelligence is becoming more prevalent in healthcare, as in many other areas. Artificial intelligence is a method that aims to solve complex problems by using computers to mathematically simulate the way the brain works. In this article, we compile and share information about a deep learning model developed for the detection and classification of pressure injuries. Deep learning can operate on many types of data. Convolutional Neural Networks (CNN) prefer images because they can handle 2D arrays. In this case, the images, annotated according to the National Pressure Injury Advisory Panel pressure injury classification system, have been fed into a deep learning model using CNN. The developed CNN model has a 97% success in detecting and classifying pressure injuries, and as more images are collected and fed into the CNN, the prediction accuracy will increase. This deep learning model allows for the automatic detection and classification of pressure injuries, an indicator of health outcomes, at an early stage and for quick and accurate intervention. In this context, it is expected that the quality of nursing care will increase, the prevalence of pressure injury will decrease, and the economic burden of this health problem will decrease.

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A Systematic Literature Review of Practical Virtual and Augmented Reality Solutions in Surgery

Cited by 14 publications

References 79 publications

The Effect of Showing Images of the Foetus with the Virtual Reality Glass During Labour Process on Labour Pain, Birth Perception and Anxiety

The Effect of Showing Images of the Foetus with the Virtual Reality Glass During Labour Process on Labour Pain, Birth Perception and Anxiety

Virtual Reality and Electrodermal Activity to Support Mild Cognitive Impairment: A Systematic Literature Review

Prediction and classification of pressure injuries by deep learning

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