Acceptance of physical activity virtual reality games by residents of long-term care facilities: A qualitative study.

BACKGROUND: Little is known about the experience and the social and contextual factors influencing the acceptance of virtual reality (VR) physical activity games among long-term care (LTC) residents. Our study aims to address this research gap by investigating the unique experience of older adults with VR games. The findings will provide valuable insights into the factors influencing VR acceptance among LTC residents and help design inclusive VR technology that meets their needs and improves physical activity (PA) and well-being. OBJECTIVE: We aimed to: (1) investigate how participants experience VR exergames and the meaning they associate with their participation; and (2) examine the factors that influence the participant's experience in VR exergames and explore how these factors affect the overall experience. METHODS: We used a qualitative approach that follows the principles of the Interpretive Description methodology. Selective Optimization and Compensation (SOC) theory, Socioemotional Selectivity theory (SST) and technology acceptance models underpinned the theoretical foundations of this study. We conducted semi-structured interviews with participants. 19 Participants of a LTC were interviewed: five residents and ten tenants, aged 65 to 93 years (8 female and 7 male) and four staff members. Interviews ranged from 15 to 30 minutes and were transcribed verbatim and were analyzed using thematic analysis. RESULTS: We identified four themes based on older adults' responses that reflected their unique VR gaming experience, including (1) enjoyment, excitement, and the novel environment; (2) PA and motivation to exercise; (3) social connection and support; and (4) individual preferences and challenges. Three themes were developed based on the staff members' data to capture their perspective on the factors that influence the acceptance of VR among LTC resident including (1) relevance and personalization of the games; (2) training and guidance; and (3) organizational and individual barriers. CONCLUSIONS: VR gaming experiences are enjoyable exciting, and novel for LTC residents and tenants and can provide physical, cognitive, social, and motivational benefits for them. Proper guidance and personalized programs can increase understanding and familiarity with VR, leading to a higher level of acceptance and engagement. Our findings emphasize the significance of social connection and support in promoting acceptance and enjoyment of VR gaming among older adults. Incorporating social theories of aging helps to gain a better understanding of how aging-related changes influence technology acceptance among older adults. This approach can inform the development of technology that better meets their needs and preferences.

First deployment of artificial intelligence recommendations in orthopedic surgery.

Scant research has delved into the non-clinical facets of artificial intelligence (AI), concentrating on leveraging data to enhance the efficiency of healthcare systems and operating rooms. Notably, there is a gap in the literature regarding the implementation and outcomes of AI solutions. The absence of published results demonstrating the practical application and effectiveness of AI in domains beyond clinical settings, particularly in the field of surgery, served as the impetus for our undertaking in this area. Within the realm of non-clinical strategies aimed at enhancing operating room efficiency, we characterize OR efficiency as the capacity to successfully perform four uncomplicated arthroplasty surgeries within an 8-h timeframe. This Community Case Study addresses this gap by presenting the results of incorporating AI recommendations at our clinical institute on 228 patient arthroplasty surgeries. The implementation of a prescriptive analytics system (PAS), utilizing supervised machine learning techniques, led to a significant improvement in the overall efficiency of the operating room, increasing it from 39 to 93%. This noteworthy achievement highlights the impact of AI in optimizing surgery workflows.

Impact of dance interventions on the symptoms of dementia: A mixed-methods systematic review.

OBJECTIVES: This mixed-methods systematic review determined the impact of dance interventions on symptoms of Alzheimer's disease and related dementias (ADRD) among persons living in residential care. METHODS: Seven databases (Medline, EMBASE, CINAHL, PsycINFO, Web of Science, Ageline, and AMED) were searched. Studies published before June 2022 that investigated the impact of dance interventions on symptoms of ADRD were eligible for inclusion. Risk of bias was assessed using CASP, ROBINS-I, and ROB-2. Quantitative and qualitative objectives provided a convergent segregate narrative synthesis for the review. The review protocol was registered on PROSPERO (CRD42021220535). RESULTS: Two quantitative and two qualitative studies met the inclusion criteria. Dance interventions decreased levels of agitation and aggression, and improved behavioural and psychological symptoms. CONCLUSIONS: Studies suggest that dance interventions reduce the symptoms of dementia through increased expression, emotions, and improved relationships for persons with ADRD. However, the small number of included studies limits these conclusions.

Assessing virtual reality acceptance in long-term care facilities: a quantitative study with older adults.

PURPOSE: Our study aimed to investigate the factors associated with the acceptance of virtual reality (VR) games among older adults living in LTC, with a particular emphasis on identifying social and individual factors that have been overlooked in existing technology acceptance models. MATERIALS AND METHODS: We conducted VR gaming sessions, followed by a composite questionnaire to explore the factors associated with the acceptance of VR games among residents of LTC with a focus on technology acceptance models (TAM) and social factors derived from Selective Optimization with Compensation (SOC) theory and Socioemotional Selectivity Theory (SST). RESULTS: We studied 20 older adults aged 65 and older. Participants were moderately sedentary, with the majority of them having prior gaming experience. Participants with prior gaming experience had higher mean scores in most SOC theory and SST subscales, except for elective selection. Participants perceived the technology as useful and easy to use, with no heightened gaming-related anxiety. Significant correlations were found between perceived ease of use and selection strategies, and between attitudes towards gaming and elective selection strategies. No significant score differences were observed between male and female participants. CONCLUSIONS: The positive correlation between VR acceptance and using SOC strategies suggests a positive response to straightforward experiences. Our study highlights VR exergaming's potential benefits for encouraging LTC residents' engagement in valued activities and pursuing goals. Moreover, social theories of aging can inform technology acceptance and guide the design and marketing of VR exergames to better suit older adults' needs and preferences in LTC.IMPLICATIONS FOR REHABILITATIONThe findings of this study have important implications for rehabilitation programs aimed at enhancing physical activity (PA) and engagement among older adults living in long-term care (LTC) facilities. The use of virtual reality (VR) games can be an important tool to promote PA and improve the overall well-being of LTC residents. Based on the results, the following implications can be drawn:Integrating VR exergaming in rehabilitation:The positive perception of VR technology's usefulness and ease of use among older adults in LTC suggests that VR exergaming can be effectively integrated into rehabilitation programs. Healthcare professionals and rehabilitation specialists in LTC facilities can consider incorporating VR-based exercise routines and gaming sessions to motivate and engage residents in physical activities. By doing so, they can create enjoyable and interactive rehabilitation experiences that may lead to improved adherence to exercise regimens.Addressing social factors for VR acceptance:Our study highlights the significance of social factors derived from theories of aging, such as Selective Optimization with Compensation (SOC) and Socioemotional Selectivity Theory (SST), in influencing VR acceptance among LTC residents. Rehabilitation programs should take into account these social aspects and create a supportive and encouraging environment for older adults to engage with VR exergames. Encouraging social interactions and providing opportunities for residents to share their experiences with VR gaming may enhance acceptance and overall engagement.Tailoring VR exergames for older adults:The correlation between VR acceptance and the use of SOC strategies indicates that customized experiences may be well-received by LTC residents. Game developers and rehabilitation specialists should consider designing VR exergames that align with the specific preferences and needs of older adults. This could involve providing choices and options for users to optimize their gaming experiences based on their individual abilities and interests.Recognizing gaming experience:Our study highlights that prior gaming experience positively influenced participants' attitudes towards VR gaming. Rehabilitation professionals should acknowledge and leverage this prior experience when introducing VR exergaming to older adults in LTC. By incorporating elements familiar to older adults or providing guidance for those new to gaming, rehabilitation programs can foster a more seamless and enjoyable transition to VR exergames.Promoting goal pursuit and valued activities:Our study suggests that VR exergaming has the potential to encourage LTC residents' engagement in valued activities and goal pursuit. Rehabilitation programs can utilize VR exergaming as a means to help residents achieve specific rehabilitation goals and engage in activities that are meaningful to them. This approach can contribute to a sense of purpose and satisfaction in the rehabilitation process.Overall, the integration of VR exergaming in rehabilitation for older adults in LTC facilities has promising implications for improving physical activity levels, enhancing engagement, and addressing the holistic well-being of residents. By considering the social factors influencing VR acceptance and tailoring experiences to individual preferences, rehabilitation professionals can optimize the potential benefits of VR technology in LTC settings.

Acceptance of physical activity virtual reality games by residents of long-term care facilities: a scoping review.

PURPOSE: This scoping review aims to identify evidence on older adults' acceptance of PA VR games in LTC facilities, describe research designs used, define key acceptance concepts, and identify knowledge gaps for future research. MATERIALS AND METHODS: Following Arksey and O'Malley's framework, data from published and unpublished articles (Jan 2000-May 2023) were collected. Twelve databases and additional sources were searched for studies on LTC residents (≥65 years), PA video games (including VR and console games), acceptance, and attitudes. Data extraction included article details, design, population, intervention, outcomes, and limitations. RESULTS: Five studies met inclusion criteria from 1628 initial titles. They assessed acceptance of PA VR games among older adults in LTC facilities, showing varying levels of acceptance. Most studies used analytical designs, including RCTs. Key concepts of VR acceptance were poorly defined, with only one study using a validated TAM questionnaire. Knowledge gaps highlight the need for further research to understand PA VR acceptance among older adults in LTC facilities. CONCLUSION: Validated acceptance questionnaires are needed in study of VR acceptance by older adults. Use of qualitative and quantitative methods can enhance understanding of technology acceptance, alongside exploration of individual, environmental, and age-related factors. Detailed reporting of VR interventions is recommended to comprehend acceptance factors.

Enhancing engagement: We suggest that physical activity (PA) virtual reality (VR) games can improve engagement among long-term care (LTC) residents. By providing a novel approach to rehabilitation, PA VR games have the potential to increase motivation and participation, leading to improved outcomes.Promoting physical and cognitive stimulation: VR games offer opportunities for both physical and cognitive stimulation. By integrating these games into rehabilitation programs, we can provide a more engaging and interactive experience for individuals undergoing rehabilitation. This can contribute to motor skills development, balance training, cognitive function, and overall well-being.Addressing barriers to rehabilitation: Traditional rehabilitation approaches may face various barriers, such as lack of interest, adherence issues, or limited resources. The use of VR games can help overcome some of these barriers by offering a more enjoyable and accessible rehabilitation experience. This is particularly beneficial for individuals with mobility limitations or those residing in LTC facilities.In summary, our study highlights the potential of incorporating VR games into rehabilitation settings. By implementing these findings, we can improve the acceptance and efficiency of rehabilitation practices, leading to better rehabilitation outcomes for individuals.

Leveraging machine learning and prescriptive analytics to improve operating room throughput.

Successful days are defined as days when four cases were completed before 3:45pm, and overtime hours are defined as time spent after 3:45pm. Based on these definitions and the 460 unsuccessful days isolated from the dataset, 465 hours, 22 minutes, and 30 seconds total overtime hours were calculated. To reduce the increasing wait lists for hip and knee surgeries, we aim to verify whether it is possible to add a 5th surgery, to the typical 4 arthroplasty surgery per day schedule, without adding extra overtime hours and cost at our clinical institution. To predict 5th cases, 301 successful days were isolated and used to fit linear regression models for each individual day. After using the models' predictions, it was determined that increasing performance to a 77% success rate can lead to approximately 35 extra cases per year, while performing optimally at a 100% success rate can translate to 56 extra cases per year at no extra cost. Overall, this shows the extent of resources wasted by overtime costs, and the potential for their use in reducing long wait times. Future work can explore optimal staffing procedures to account for these extra cases.

Effects of virtual reality guided meditation in older adults: the protocol of a pilot randomized controlled trial.

Background: Virtual reality (VR) based meditation has been shown to help increase relaxation and decrease anxiety and depression in younger adults. However, this has not been studied in Randomized Controlled Trials (RCT) in the older adult population. The aim of this RCT is to assess the feasibility and acceptability of a VR-guided meditation intervention for community-dwelling older adults and its effect on stress and mental health. Methods: We will recruit 30 participants aged ≥ 60 years, whose perceived stress score (PSS) is > 14 (moderate stress), and randomize them 1:1 to the intervention or control waitlist group. The intervention will involve exposure to eight 15-min VR-guided meditation sessions distributed twice weekly for 4-weeks. Two modalities will be offered: in-home and at the hospital. Data analysis: Baseline and post-intervention assessments will evaluate perceived stress, anxiety, depression, sleep quality, quality of life, and mindfulness skills. Analyses will employ mixed methods repeated ANOVA tests. Qualitative analyses through semi-structured interviews and participant observation will be used to assess participants' experiences. Study outcomes include: (A) feasibility and acceptability compared to a waitlist control (B) stress, using the Perceived Stress Scale (PSS); (C) anxiety, and depression, using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9); (D) insomnia, quality of life and mindfulness skills, using the Athens Insomnia Scale (AIS), Quality of Life Questionnaire (EQ-5D-5L) and Five Facets Mindfulness Questionnaire Short Forms (FFMQ-SF), respectively. We will also measure immersive tendencies, sickness and sense of presence using the Simulator Sickness Questionnaire (SSQ) and the Presence Questionnaire (PQ). Discussion: Virtual reality-guided meditation could be an acceptable, feasible, safe, and cost-effective novel alternative health intervention for improving older adults' mental health.Clinical trial registration: NCT05315609 at https://clinicaltrials.gov.

Use of multidisciplinary positive deviance seminars to improve efficiency in a high-volume arthroplasty practice: a pilot study.

BACKGROUND: Positive deviance (PD) seminars, which have shown excellent results in improving the quality of surgical practices, use individual performance feedback to identify team members who outperform their peers; the strategies from those with exemplary performance are used to improve team members' practices. Our study aimed to use the PD approach with arthroplasty surgeons and nurses to identify multidisciplinary strategies and recommendations to improve operating room (OR) efficiency. METHODS: We recruited 5 surgeons who performed high-volume primary arthroplasty and had participated in 4-joint rooms since 2012, and 29 nurses who had participated in 4-joint rooms and in at least 16 cases in our data set. Three 1-hour PD sessions were held in February and March 2021: 1 with surgeons, 1 with nurses, and 1 with both surgeons and nurses to select recommendations for implementation. The sessions were led by a member of the nonorthopedic surgical faculty who was familiar with the subjects discussed and with PD seminars. To determine the success of the recommendations, we compared OR efficiency before and after implementation. We defined success as performance of 4 joint procedures within 8 hours. RESULTS: Eleven recommendations were recorded from the session with nurses and 7 from the session with surgeons, of which 11 were selected for implementation. During the month after implementation, there were great improvements across all time intervals of surgical procedures, with the greatest improvements seen in mean anesthesia preparation time in the room (4.51 min [26.3%]), mean procedure duration (9.75 min [14.0%]) and mean anesthesia finish time (5.78 min [44.0%]) (all p < 0.001). The total time saved per day was 49.84 minutes; this led to a success rate of 69.0%, a relative increase of 73.8% from our 2012-2020 success rate of 39.7% (p < 0.001). CONCLUSION: The recommendations and increased motivation owing to the individualized feedback reduced time spent per case, allowing more days to finish on time. Positive deviance seminars offer an inexpensive, efficient and collegial means for process improvement in the OR.

Artificial intelligence-driven prescriptive model to optimize team efficiency in a high-volume primary arthroplasty practice.

PURPOSE: We aimed to improve OR efficiency using machine learning (ML) to find relevant metrics influencing surgery time success and team performance on efficiency to create a model which incorporated team, patient, and surgery-related factors. METHODS: From 2012 to 2020, five surgeons, 44 nurses, and 152 anesthesiologists participated in 1199 four joint days (4796 cases): 1461 THA, 1496 TKA, 652 HR, 242 UKA, and 945 others. Patients were 2461f:2335 m; age, 64.1; BMI, 29.93; and ASA, 2.45. Surgical Success was defined as completing four joints within an eight hour shift using one OR. Time data was recorded prospectively using Surgical Information Management Systems. Hospital records provided team, patient demographics, adverse events, and anesthetic. Data mining identified patterns and relationships in higher dimensions. Predictive analytics used ML ranking algorithm to identify important metrics and created decision tree models for benchmarks and success probability. RESULTS: Five variables predicted success: anaesthesia preparation time, surgical preparation time, time of procedure, anesthesia finish time, and type of joint replacement. The model determined success rate with accuracy of 72% and AUC = 0.72. Probability of success based on mean performance was 77-89% (mean-median) if APT 14-15 minutes, PT 68-70 minutes, AFT four to five minutes, and turnover 25-27 minutes. With the above benchmarks maintained, success rate was 59% if surgeon exceeded 71.5-minutes PT or 89% if 64-minutes procedure time or 66% when anesthesiologist spent 17-19.5 minutes on APT. CONCLUSION: AI-ML predicted OR success without increasing resources. Benchmarks track OR performance, demonstrate effects of strategic changes, guide decisions, and provide teamwork improvement opportunities.

Factors influencing delays and overtime during surgery: a descriptive analytics for high volume arthroplasty procedures.

The aim of this article is to analyze factors influencing delays and overtime during surgery. We utilized descriptive analytics and divided the factors into three levels. In level one, we analyzed each surgical metrics individually and how it may influence the Surgical Success Rate (SSR) of each operating day. In level two, we compared up to three metrics at once, and in level three, we analyzed four metrics to identify more complex patterns in data including correlations. Within each level, factors were categorized as patient, surgical team, and time specific. Retrospective data on 788 high volume arthroplasty procedures was compiled and analyzed from the 4-joint arthroplasty operating room at our institution. Results demonstrated that surgical team performance had the highest impact on SSR whereas patient metrics had the least influence on SSR. Additionally, beginning the surgical day on time has a prominent effect on the SSR. Finally, the experience of the surgeon had almost no impact on the SSR. In conclusion, we gathered a list of insights that can help influence the re-allocation of resources in daily clinical practice to offset inefficiencies in arthroplasty surgeries.

Isolating together during COVID-19: Results from the Telehealth Intervention Program for older adults.

Background: A pressing challenge during the COVID-19 pandemic and beyond is to provide accessible and scalable mental health support to isolated older adults in the community. The Telehealth Intervention Program for Older Adults (TIP-OA) is a large-scale, volunteer-based, friendly telephone support program designed to address this unmet need. Methods: A prospective cohort study of 112 TIP-OA participants aged ≥60 years old was conducted in Quebec, Canada (October 2020-June 2021). The intervention consisted of weekly friendly phone calls from trained volunteers. The primary outcome measures included changes in scores of stress, depression, anxiety, and fear surrounding COVID-19, assessed at baseline, 4 and 8-weeks. Additional subgroup analyses were performed with participants with higher baseline scores. Results: The subgroup of participants with higher baseline depression scores (PHQ9 ≥10) had significant improvements in depression scores over the 8-week period measured [mean change score = -2.27 (±4.76), 95%CI (-3.719, -0.827), p = 0.003]. Similarly, participants with higher baseline anxiety scores (GAD7 ≥10) had an improvement over the same period, which, approached significance (p = 0.06). Moreover, despite peaks in the pandemic and related stressors, our study found no significant (p ≥ 0.09) increase in stress, depression, anxiety or fear of COVID-19 scores. Discussion: This scalable, volunteer-based, friendly telephone intervention program was associated with decreased scores of depression and anxiety in older adults who reported higher scores at baseline (PHQ 9 ≥10 and GAD7 ≥10).

A Brief Report on Reviews of Existing Creative Art-Based Interventions in Dementia Care From 2010-2020.

The following brief report provides an overview of previously published reviews in the context of creative arts-based interventions for persons with dementia. A total of 22 review articles were identified and summarized. Next steps are suggested for future studies that may wish to a) develop a new review, or b) create new studies filling in the gaps identified by the authors in this report.

Artificial Intelligence to Analyze the Cortical Thickness Through Age.

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.

Development of a Smart Hallway for Marker-Less Human Foot Tracking and Stride Analysis.

OBJECTIVE: In this research, a marker-less 'smart hallway' is proposed where stride parameters are computed as a person walks through an institutional hallway. Stride analysis is a viable tool for identifying mobility changes, classifying abnormal gait, estimating fall risk, monitoring progression of rehabilitation programs, and indicating progression of nervous system related disorders. METHODS: Smart hallway was build using multiple Intel RealSense D415 depth cameras. A novel algorithm was developed to track a human foot using combined point cloud data obtained from the smart hallway. A method was implemented to separate the left and right leg point cloud data, then find the average foot dimensions. Foot tracking was achieved by fitting a box with average foot dimensions to the foot, with the box's base on the foot's bottom plane. A smart hallway with this novel foot tracking algorithm was tested with 22 able-bodied volunteers by comparing marker-less system stride parameters with Vicon motion analysis output. RESULTS: With smart hallway frame rate at approximately 60fps, temporal stride parameter absolute mean differences were less than 30ms. Random noise around the foot's point cloud was observed, especially during foot strike phases. This caused errors in medial-lateral axis dependent parameters such as step width and foot angle. Anterior-posterior dependent (stride length, step length) absolute mean differences were less than 25mm. CONCLUSION: This novel marker-less smart hallway approach delivered promising results for stride analysis with small errors for temporal stride parameters, anterior-posterior stride parameters, and reasonable errors for medial-lateral spatial parameters.

Generating X-ray Images from Point Clouds Using Conditional Generative Adversarial Networks.

Simulating medical images such as X-rays is of key interest to reduce radiation in non-diagnostic visualization scenarios. Past state of the art methods utilize ray tracing, which is reliant on 3D models. To our knowledge, no approach exists for cases where point clouds from depth cameras and other sensors are the only input modality. We propose a method for estimating an X-ray image from a generic point cloud using a conditional generative adversarial network (CGAN). We train a CGAN pix2pix to translate point cloud images into X-ray images using a dataset created inside our custom synthetic data generator. Additionally, point clouds of multiple densities are examined to determine the effect of density on the image translation problem. The results from the CGAN show that this type of network can predict X-ray images from points clouds. Higher point cloud densities outperformed the two lowest point cloud densities. However, the networks trained with high-density point clouds did not exhibit a significant difference when compared with the networks trained with medium densities. We prove that CGANs can be applied to image translation problems in the medical domain and show the feasibility of using this approach when 3D models are not available. Further work includes overcoming the occlusion and quality limitations of the generic approach and applying CGANs to other medical image translation problems.

Learning Conflicts for C-arm Kinematic Modeling using Artificial Intelligence.

During common surgical tasks related to orthopedic applications, it is necessary to carefully manipulate a mobile C-arm device to achieve the desired position. In this work, we propose the application of learning conflicts analysis to improve the performance of an artificial neural network to compute the inverse kinematics of a C-arm device. Using the forward kinematics equations of a C-arm device (and the respective patient table) a training set for machine learning was generated. However, as an inverse kinematics problem may have multiple solutions, it is likely that training a neural network using forward kinematics data may generate machine learning conflicts. In this sense, we show that it is possible to eliminate those C-arm positions that may represent a learning conflict for the neural network, and thus, improve the accuracy of the model. Finally, we randomly generated a suitable validation set to verify the performance of our proposed model with data different from those used for training.

pix2xray: converting RGB images into X-rays using generative adversarial networks.

PURPOSE: We propose a novel methodology for generating synthetic X-rays from 2D RGB images. This method creates accurate simulations for use in non-diagnostic visualization problems where the only input comes from a generic camera. Traditional methods are restricted to using simulation algorithms on 3D computer models. To solve this problem, we propose a method of synthetic X-ray generation using conditional generative adversarial networks (CGANs). METHODS: We create a custom synthetic X-ray dataset generator to generate image triplets for X-ray images, pose images, and RGB images of natural hand poses sampled from the NYU hand pose dataset. This dataset is used to train two general-purpose CGAN networks, pix2pix and CycleGAN, as well as our novel architecture called pix2xray which expands upon the pix2pix architecture to include the hand pose into the network. RESULTS: Our results demonstrate that our pix2xray architecture outperforms both pix2pix and CycleGAN in producing higher-quality X-ray images. We measure higher similarity metrics in our approach, with pix2pix coming in second, and CycleGAN producing the worst results. Our network performs better in the difficult cases which involve high occlusion due to occluded poses or large rotations. CONCLUSION: Overall our work establishes a baseline that synthetic X-rays can be simulated using 2D RGB input. We establish the need for additional data such as the hand pose to produce clearer results and show that future research must focus on more specialized architectures to improve overall image clarity and structure.

Camera-augmented mobile C-arm (CamC): A feasibility study of augmented reality imaging in the operating room.

BACKGROUND: In orthopaedic trauma surgery, image-guided procedures are mostly based on fluoroscopy. The reduction of radiation exposure is an important goal. The purpose of this work was to investigate the impact of a camera-augmented mobile C-arm (CamC) on radiation exposure and the surgical workflow during a first clinical trial. METHODS: Applying a workflow-oriented approach, 10 general workflow steps were defined to compare the CamC to traditional C-arms. The surgeries included were arbitrarily identified and assigned to the study. The evaluation criteria were radiation exposure and operation time for each workflow step and the entire surgery. The evaluation protocol was designed and conducted in a single-centre study. RESULTS: The radiation exposure was remarkably reduced by 18 X-ray shots 46% using the CamC while keeping similar surgery times. CONCLUSIONS: The intuitiveness of the system, its easy integration into the surgical workflow, and its great potential to reduce radiation have been demonstrated.

An Augmented Reality magic mirror as additive teaching device for gross anatomy.

When preparing young medical students for clinical activity, it is indispensable to acquaint them with anatomical section images which enable them to use the clinical application of imaging methods. A new Augmented Reality Magic Mirror (AR MM) system, which provides the advantage of a novel, interactive learning tool in addition to a regular dissection course, was therefore tested and evaluated by 880 first-year medical students as part of the macroscopic anatomy course in 2015/16 at Ludwig-Maximilians-Universität (LMU) in Munich. The system consists of an RGB-D sensor as a real-time tracking device, which enables the system to link a deposited section image to the projection of the user's body, as well as a large display mimicking a real-world physical mirror. Using gesture input, the users have the ability to interactively explore radiological images in different anatomical intersection planes. We designed a tutorial during which students worked with the system in groups of about 12 and evaluated the results. Subsequently, each participant was asked to assess the system's value by filling out a Likert-scale questionnaire. The respondents approved all statements which stressed the potential of the system to serve as an additional learning resource for anatomical education. In this case, emphasis was put on active learning, 3-dimensional understanding, and a better comprehension of the course of structures. We are convinced that such an AR MM system can be beneficially installed into anatomical education in order to prepare medical students more effectively for the clinical standards and for more interactive, student-centered learning.