A Mobile Geo-Communication Dataset for Physiology-Aware DASH in Rural Ambulance Transport

Use of telecommunication technologies for remote, continuous monitoring of patients can enhance effectiveness of emergency ambulance care during transport from rural areas to a regional center hospital. However, the communication along the various routes in rural areas may have wide bandwidth ranges from 2G to 4G; some regions may have only lower satellite bandwidth available. Bandwidth fluctuation together with real-time communication of various clinical multimedia pose a major challenge during rural patient ambulance transport.; AB@The availability of a pre-transport route-dependent communication bandwidth database is an important resource in remote monitoring and clinical multimedia transmission in rural ambulance transport. Here, we present a geo-communication dataset from extensive profiling of 4 major US mobile carriers in Illinois, from the rural location of Hoopeston to the central referral hospital center at Urbana. In collaboration with Carle Foundation Hospital, we developed a profiler, and collected various geographical and communication traces for realistic emergency rural ambulance transport scenarios. Our dataset is to support our ongoing work of proposing "physiology-aware DASH", which is particularly useful for adaptive remote monitoring of critically ill patients in emergency rural ambulance transport. It provides insights on ensuring higher Quality of Service (QoS) for most critical clinical multimedia in response to changes in patients' physiological states and bandwidth conditions. Our dataset is available online for research community.Comment: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys'17), Pages 158-163, Taipei, Taiwan, June 20 - 23, 201

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

Systematic review and meta-analysis of the growth and rupture rates of small abdominal aortic aneurysms: implications for surveillance intervals and their cost-effectiveness.

BACKGROUND: Small abdominal aortic aneurysms (AAAs; 3.0-5.4 cm in diameter) are usually asymptomatic and managed by regular ultrasound surveillance until they grow to a diameter threshold (commonly 5.5 cm) at which surgical intervention is considered. The choice of appropriate surveillance intervals is governed by the growth and rupture rates of small AAAs, as well as their relative cost-effectiveness. OBJECTIVES: The aim of this series of studies was to inform the evidence base for small AAA surveillance strategies. This was achieved by literature review, collation and analysis of individual patient data, a focus group and health economic modelling. DATA SOURCES: We undertook systematic literature reviews of growth rates and rupture rates of small AAAs. The databases MEDLINE, EMBASE on OvidSP, Cochrane Central Register of Controlled Trials 2009 Issue 4, ClinicalTrials.gov, and controlled-trials.com were searched from inception up until the end of 2009. We also obtained individual data on 15,475 patients from 18 surveillance studies. REVIEW METHODS: Systematic reviews of publications identified 15 studies providing small AAA growth rates, and 14 studies with small AAA rupture rates, up to December 2009 (later updated to September 2012). We developed statistical methods to analyse individual surveillance data, including the effects of patient characteristics, to inform the choice of surveillance intervals and provide inputs for health economic modelling. We updated an existing health economic model of AAA screening to address the cost-effectiveness of different surveillance intervals. RESULTS: In the literature reviews, the mean growth rate was 2.3 mm/year and the reported rupture rates varied between 0 and 1.6 ruptures per 100 person-years. Growth rates increased markedly with aneurysm diameter, but insufficient detail was available to guide surveillance intervals. Based on individual surveillance data, for each 0.5-cm increase in AAA diameter, growth rates increased by about 0.5 mm/year and rupture rates doubled. To control the risk of exceeding 5.5 cm to below 10% in men, on average a 7-year surveillance interval is sufficient for a 3.0-cm aneurysm, whereas an 8-month interval is necessary for a 5.0-cm aneurysm. To control the risk of rupture to below 1%, the corresponding estimated surveillance intervals are 9 years and 17 months. Average growth rates were higher in smokers (by 0.35 mm/year) and lower in patients with diabetes (by 0.51 mm/year). Rupture rates were almost fourfold higher in women than men, doubled in current smokers and increased with higher blood pressure. Increasing the surveillance interval from 1 to 2 years for the smallest aneurysms (3.0-4.4 cm) decreased costs and led to a positive net benefit. For the larger aneurysms (4.5-5.4 cm), increasing surveillance intervals from 3 to 6 months led to equivalent cost-effectiveness. LIMITATIONS: There were no clear reasons why the growth rates varied substantially between studies. Uniform diagnostic criteria for rupture were not available. The long-term cost-effectiveness results may be susceptible to the modelling assumptions made. CONCLUSIONS: Surveillance intervals of several years are clinically acceptable for men with AAAs in the range 3.0-4.0 cm. Intervals of around 1 year are suitable for 4.0-4.9-cm AAAs, whereas intervals of 6 months would be acceptable for 5.0-5.4-cm AAAs. These intervals are longer than those currently employed in the UK AAA screening programmes. Lengthening surveillance intervals for the smallest aneurysms was also shown to be cost-effective. Future work should focus on optimising surveillance intervals for women, studying whether or not the threshold for surgery should depend on patient characteristics, evaluating the usefulness of surveillance for those with aortic diameters of 2.5-2.9 cm, and developing interventions that may reduce the growth or rupture rates of small AAAs. FUNDING: The National Institute for Health Research Health Technology Assessment programme

An attention residual u-net with differential preprocessing and geometric postprocessing: Learning how to segment vasculature including intracranial aneurysms

Objective Intracranial aneurysms (IA) are lethal, with high morbidity and mortality rates. Reliable, rapid, and accurate segmentation of IAs and their adjacent vasculature from medical imaging data is important to improve the clinical management of patients with IAs. However, due to the blurred boundaries and complex structure of IAs and overlapping with brain tissue or other cerebral arteries, image segmentation of IAs remains challenging. This study aimed to develop an attention residual U-Net (ARU-Net) architecture with differential preprocessing and geometric postprocessing for automatic segmentation of IAs and their adjacent arteries in conjunction with 3D rotational angiography (3DRA) images. Methods The proposed ARU-Net followed the classic U-Net framework with the following key enhancements. First, we preprocessed the 3DRA images based on boundary enhancement to capture more contour information and enhance the presence of small vessels. Second, we introduced the long skip connections of the attention gate at each layer of the fully convolutional decoder-encoder structure to emphasize the field of view (FOV) for IAs. Third, residual-based short skip connections were also embedded in each layer to implement in-depth supervision to help the network converge. Fourth, we devised a multiscale supervision strategy for independent prediction at different levels of the decoding path, integrating multiscale semantic information to facilitate the segmentation of small vessels. Fifth, the 3D conditional random field (3DCRF) and 3D connected component optimization (3DCCO) were exploited as postprocessing to optimize the segmentation results. Results Comprehensive experimental assessments validated the effectiveness of our ARU-Net. The proposed ARU-Net model achieved comparable or superior performance to the state-of-the-art methods through quantitative and qualitative evaluations. Notably, we found that ARU-Net improved the identification of arteries connecting to an IA, including small arteries that were hard to recognize by other methods. Consequently, IA geometries segmented by the proposed ARU-Net model yielded superior performance during subsequent computational hemodynamic studies (also known as patient-specific computational fluid dynamics [CFD] simulations). Furthermore, in an ablation study, the five key enhancements mentioned above were confirmed. Conclusions The proposed ARU-Net model can automatically segment the IAs in 3DRA images with relatively high accuracy and potentially has significant value for clinical computational hemodynamic analysis