As the healthcare enterprise is adopting novel imaging and health-assessment technologies, we are facing unprecedented requirements in information sharing, patient empowerment, and care coordination within the system. Medical experts not only within US, but around the world should be empowered through collaboration capabilities on 3D data to enable solutions for complex medical problems that will save lives. The fast-growing number of 3D medical ‘images' and their derivative information must be shared across the healthcare enterprise among stakeholders with vastly different perspectives and different needs. The demand for 3D data visualization is driving the need for increased accessibility and sharing of 3D medical image presentations, including their annotations and their animations. As patients have to make decisions about their health, empowering them with the right tools to understand a medical procedure is essential both in the decision-making process and for knowledge sharing.

This is a work in progress paper that describes a novel endoscope interface designed for use in an immersive virtual reality surgical simulator. We use an affordable off the shelf head mounted display to recreate the operating theatre environment. A hand held controller has been adapted so that it feels like the trainee is holding an endoscope controller with the same functionality. The simulator allows the endoscope shaft to be inserted into a virtual patient and pushed forward to a target position. The paper describes how we have built this surgical simulator with the intention of carrying out a full clinical study in the near future.

Virtual reality entertainment and serious games popularity has continued to rise but the processes for level design for VR games has not been adequately researched. Our paper contributes LevelEd VR; a generic runtime virtual reality level editor that supports the level design workflow used by developers and can potentially support user generated content. We evaluated our LevelEd VR application and compared it to an existing workflow of Unity on a desktop. Our current research indicates that users are accepting of such a system, and it has the potential to be preferred over existing workflows for VR level design. We found that the primary benefit of our system is an improved sense of scale and perspective when creating the geometry and implementing gameplay. The paper also contributes some best practices and lessons learned from creating a complex virtual reality tool, such as LevelEd VR.

Human reliance on interconnected devices has given rise to a massive increase in cyber activities. There are about 17 billion interconnected devices in our World of about 8 billion people. Like the physical world, the cyber world is not void of entities whose activities, malicious or not, could be detrimental to other users who remain vulnerable as a result of their existence within cyberspace. Developments such as the introduction of 5G networks which advances communication speed among interconnected devices, undoubtedly proffer solutions for human living as well as adversely impacting systems. Vulnerabilities in applications embedded in devices, hardware deficiencies, user errors, are some of the loopholes that are exploited. Studies have revealed humans as weakest links in the cyber-chain, submitting that consistent implementation of cyber awareness programs would largely impact cybersecurity. Cyber-active systems have goals that compete with the implementation of cyber awareness programs, within limited resources. It is desirable to have cyber awareness systems that can be tailored around specific needs and considerations for important factors. This paper presents a system that aims to promote user awareness through a flexible, accessible, and cost-effective design. The system implements steps in a user awareness cycle, that considers human-factor (HF) and HF related root causes of cyber-attacks. We introduce a new user testing tool, adaptable for administering cybersecurity test questions for varying levels and categories of users. The tool was implemented experimentally by engaging cyber users within UK. Schemes and online documentations by UK Cybersecurity organisations were harnessed for assessing and providing relevant recommendations to participants. Results provided us with values representing each participants’ notional level of awareness which were subjected to a paired-T test for comparison with values derived in an automated assessment. This pilot study provides valuable details for projecting the efficacy of the system towards improving human influence in cybersecurity.

Background: Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. Results: Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). Conclusions: The models are used to investigate how different degrees of patient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted.

We present a novel self-supervised framework for monocular image depth learning and confidence estimation. Our framework reduces the amount of ground truth annotation data required for training Convolutional Neural Networks (CNNs), which is often a challenging problem for the fast deployment of CNNs in many computer vision tasks. Our DepthNet adopts a novel fully differential patch-based cost function through the Zero-Mean Normalized Cross Correlation (ZNCC) to take multi-scale patches as matching and learning strategies. This approach greatly increases the accuracy and robustness of the depth learning. Whilst the proposed patch-based cost function naturally provides a 0-to-1 confidence, it is then used to self-supervise the training of a parallel network for confidence map learning and estimation by exploiting the fact that ZNCC is a normalized measure of similarity which can be approximated as the confidence of the depth estimation. Therefore, the proposed corresponding confidence map learning and estimation operate in a self-supervised manner and is a parallel network to the DepthNet. Evaluation on the KITTI depth prediction evaluation dataset and Make3D dataset show that our method outperforms the state-of-the-art results.

Background: In North America and three European countries Translational Medicine (TM) funding has taken center stage as the National Institutes of Health (NIH), for example, has come to recognize that delays are common place in completing clinical trials based upon benchside advancements. Recently, there are several illustrative examples whereby the translation of research had untoward outcomes requiring immediate action. Methods: Focus more on three-dimensional (3D) simulation, biomarkers, and Artificial Intelligence may allow orthopaedic surgeons to predict the ideal practices before orthopaedic surgery. Using the best medical imaging techniques may improve the accuracy and precision of tumor resections. Results: This article is directed at the young surgeon scientist and in particular orthopaedic residents and all other junior physicians in training to help them better understand TM and position themselves in career paths and hospital systems that strive for optimal TM. It serves to hasten the movement of knowledge garnered from the benchside and move it quickly to the bedside. Conclusions: Communication is ongoing in a bidirectional format. It is anticipated that more and more medical Centers and institutions will adopt TM models of healthcare delivery.

Surgical smoke removal algorithms can improve the quality of intra-operative imaging and reduce hazards in image-guided surgery, a highly desirable post-process for many clinical applications. These algorithms also enable effective computer vision tasks for future robotic surgery. In this paper, we present a new unsupervised learning framework for high-quality pixel-wise smoke detection and removal. One of the well recognized grand challenges in using convolutional neural networks (CNNs) for medical image processing is to obtain intra-operative medical imaging datasets for network training and validation, but availability and quality of these datasets are scarce. Our novel training framework does not require ground-truth image pairs. Instead, it learns purely from computer-generated simulation images. This approach opens up new avenues and bridges a substantial gap between conventional non-learning based methods and which requiring prior knowledge gained from extensive training datasets. Inspired by the Generative Adversarial Network (GAN), we have developed a novel generative-collaborative learning scheme that decomposes the de-smoke process into two separate tasks: smoke detection and smoke removal. The detection network is used as prior knowledge, and also as a loss function to maximize its support for training of the smoke removal network. Quantitative and qualitative studies show that the proposed training framework outperforms the state-of-the-art de-smoking approaches including the latest GAN framework (such as PIX2PIX). Although trained on synthetic images, experimental results on clinical images have proved the effectiveness of the proposed network for detecting and removing surgical smoke on both simulated and real-world laparoscopic images.

With the rise in popularity of serious games there is an increasing demand for virtual environments based on real-world locations. Emergency evacuation or fire safety training are prime examples of serious games that would benefit from accurate location depiction together with any application involving personal space. However, creating digital indoor models of real-world spaces is a difficult task and the results obtained by applying current techniques are often not suitable for use in real-time virtual environments. To address this problem, we have developed an application called LevelEd AR that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability. Finally, we carried out a preliminary case study that demonstrates how LevelEd AR can be successfully used as part of current industry workflows for serious games level design.

We developed an application that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone to assist with serious games level design. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability.