Search and Evaluation of Coevolving Problem and Solution Spaces in a Complex Healthcare Design Science Research Project.

This research employs design ethnography to study the design process of a design science research (DSR) project conducted over eight years. The DSR project focuses on chronic wounds and how Information Technology (IT) might support the management of those wounds. Since this is a new and complex problem not previously addressed by IT, it requires an exploration and discovery process. As such, we found that traditional DSR methodologies were not well-suited to guiding the design process. Instead we discovered that focusing on search, and in particular, the co-evolution of the problem and solution spaces, provides a much better focus for managing the DSR design process. The presentation of our findings from the ethnographic study includes a new representation for capturing the co-evolving problem/solution spaces, an illustration of the search process and co-evolving problem/solution spaces using the DSR project we studied, the need for changes in the purpose of DSR evaluation activities when using a search-focused design process, and how our proposed process extends and augments current DSR methodologies. Studying the DSR design process generates the knowledge that research project managers need for managing and guiding a DSR project, and contributes to our knowledge of the design process for research-oriented projects. Managerial Relevance Statement: From a managerial perspective, studying the design process provides the knowledge that research project managers need for managing and guiding DSR projects. In particular, research project managers can guide the search process by understanding when and why to explore different search spaces, to expand the solutions investigated, and to focus on promising solutions and to evaluate them. Overall, this research contributes to our knowledge of design and the design process, especially for highly research-oriented problems and solutions.

Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet.

Goal: Augment a small, imbalanced, wound dataset by using semi-supervised learning with a secondary dataset. Then utilize the augmented wound dataset for deep learning-based wound assessment. Methods: The clinically-validated Photographic Wound Assessment Tool (PWAT) scores eight wound attributes: Size, Depth, Necrotic Tissue Type, Necrotic Tissue Amount, Granulation Tissue type, Granulation Tissue Amount, Edges, Periulcer Skin Viability to comprehensively assess chronic wound images. A small corpus of 1639 wound images labeled with ground truth PWAT scores was used as reference. A Semi-Supervised learning and Progressive Multi-Granularity training mechanism were used to leverage a secondary corpus of 9870 unlabeled wound images. Wound scoring utilized the EfficientNet Convolutional Neural Network on the augmented wound corpus. Results: Our proposed Semi-Supervised PMG EfficientNet (SS-PMG-EfficientNet) approach estimated all 8 PWAT sub-scores with classification accuracies and F1 scores of about 90% on average, and outperformed a comprehensive list of baseline models and had a 7% improvement over the prior state-of-the-art (without data augmentation). We also demonstrate that synthetic wound image generation using Generative Adversarial Networks (GANs) did not improve wound assessment. Conclusions: Semi-supervised learning on unlabeled wound images in a secondary dataset achieved impressive performance for deep learning-based wound grading.

A self-contained, programmable microfluidic cell culture system with real-time microscopy access.

Utilizing microfluidics is a promising way for increasing the throughput and automation of cell biology research. We present a complete self-contained system for automated cell culture and experiments with real-time optical read-out. The system offers a high degree of user-friendliness, stability due to simple construction principles and compactness for integration with standard instruments. Furthermore, the self-contained system is highly portable enabling transfer between work stations such as laminar flow benches, incubators and microscopes. Accommodation of 24 individual inlet channels enables the system to perform parallel, programmable and multiconditional assays on a single chip. A modular approach provides system versatility and allows many different chips to be used dependent upon application. We validate the system's performance by demonstrating on-chip passive switching and mixing by peristaltically driven flows. Applicability for biological assays is demonstrated by on-chip cell culture including on-chip transfection and temporally programmable gene expression.

Generation of 3D ultrasound training volumes from freehand acquired data.

We are developing a low cost ultrasound training system running on a laptop in which the user scans a generic 3D curved surface representing the patient using a 5 DoF sensor. A critical component of this system is the generation of ultrasound training image volumes, which need to cover a complete body region in order to provide a realistic scanning experience. This research attempts to develop stitching techniques to generate large global volumes from smaller overlapping volumes acquired using freehand techniques.

Personal low-cost ultrasound training system.

To meet the challenge of making realistic training opportunities in medical ultrasound readily available, a PC based low cost personal ultrasound training system has been developed. The training experience is provided by scanning a generic, curved and compliant scan surface with a sham transducer, containing position and orientation sensors, while the PC displays both a virtual subject and a virtual transducer, along with an ultrasound image.

The effect of antimicrobial treatment on mortality associated with urinary tract disease in mink kits (Neovison vison).

Mink urinary tract disease (MUTD) often presents as urolithiasis and/or cystitis and is known as an important cause of mortality in mink kits during the early growth season. Antimicrobial flock treatment has been routinely applied as preventive/therapeutic protocol on Danish mink farms with increased mortality associated with MUTD. The therapeutic effect of this treatment strategy has not previously been investigated. In this study, we applied controlled parallel group treatment trials to assess the effect of sulfadiazine/trimethoprim and amoxicillin treatment on mortality associated with MUTD in mink kits. On farm A, eight mink kits were diagnosed with MUTD post mortem in the treatment group (n = 1920, sulfadiazine/trimethoprim treatment: 30 mg/kg, q 24 h, P.O for 5 days) compared to 16 in the untreated control group (n = 1920). No significant difference in mortality associated with MUTD were found between  the treatment and the control group using the Fisher's exact test (P = 0.15). Treatment group 2 (n = 1920, amoxicillin treatment: 14 mg/kg q 24 h, P.O for 5 days) and treatment group 3 (n = 2088, amoxicillin treatment: 7.5 mg/kg q 24 h, P.O for 5 days) were investigated on farm B. Eight and four mink kits were diagnosed with MUTD post mortem in group 2 and 3, respectively. No difference between occurrence of MUTD were found between the control group and treatment group 2 (P = 0.42) or treatment group 3 (P = 0.75). No significant difference between final body weights or weight gain were found between treatment and control weighing groups on farm A or B. In conclusion, antimicrobial treatment administered in the feed showed no significant effect on weight gain or mortality associated with MUTD on the farms included in this study.

Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention.

GOAL: Chronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment. METHODS: We comprehensively score wounds based on the clinically-validated Photographic Wound Assessment Tool (PWAT), which comprehensively assesses clinically important ranges of eight wound attributes: Size, Depth, Necrotic Tissue Type, Necrotic Tissue Amount, Granulation Tissue type, Granulation Tissue Amount, Edges, Periulcer Skin Viability. We proposed a DenseNet Convolutional Neural Network (CNN) framework with patch-based context-preserving attention to assess the 8 PWAT attributes of four wound types: diabetic ulcers, pressure ulcers, vascular ulcers and surgical wounds. RESULTS: In an evaluation on our dataset of 1639 wound images, our model estimated all 8 PWAT sub-scores with classification accuracies and F1 scores of over 80%. CONCLUSIONS: Our work is the first intelligent system that autonomously grades wounds comprehensively based on criteria in the PWAT rubric, alleviating the significant burden that manual wound grading imposes on wound care nurses.

Optogenetics and Optical Tools in Automated Patch Clamping.

Automated patch clamping (APC) has been used for almost two decades to increase the throughput of electrophysiological measurements, especially in preclinical safety screening of drug compounds. Typically, cells are suctioned onto holes in planar surfaces and a stronger subsequent suction allows access to a whole cell configuration for electrical measurement of ion channel activity. The development of optogenetic tools over a wide range of wavelengths (UV to IR) provides powerful tools for improving spatiotemporal control of in vivo and in vitro experiments and is emerging as a powerful means of investigating cell networks (neuronal), single cell transduction, and subcellular pathways.Combining APC and optogenetic tools paves the way for improved investigation and control of cell kinetics and provides the opportunity for collecting robust data for new and exciting applications and therapeutic areas. Here, we present an APC optogenetics capability on the Qube Opto 384 system including experiments on light activated ion channels and photoactivated ligands.

Wireless image streaming in mobile ultrasound.

This work evaluates the feasibility of using 802.11 g ad hoc and 3G cellular broadband networks to wirelessly stream ultrasound video in real-time. Telemedicine ultrasound applications in events such as disaster relief and first-response triage can incorporate these technologies, enabling onsite medical personnel to receive assistance with diagnostic decisions by remote medical experts. The H.264 scalable video codec was used to encode echocardiographic video streams at various image resolutions (video graphics array [VGA] and quarter video graphics array [QVGA]) and frame rates (10, 15, 20, and 30 frames/s). The video stream was transmitted using 802.11 g and 3G cellular technologies, and pertinent transmission parameters such as data rate, packet loss, delay jitter, and latency were measured. 802.11 g permits high frame rate and VGA resolution and has low latency and jitter, but it is suitable only for short communication ranges, whereas the 3G cellular network allows medium to low frame rate streaming at QVGA image resolution with medium latency. However, video streaming can take place from any location with 3G service to any other site with Internet connectivity. The transmitted ultrasound video streams were subsequently recorded and evaluated by physicians with expertise in medical ultrasonography who evaluated the diagnostic value of the received video streams relative to the original videos. They expressed the opinion that image quality in the case of both 802.11 g and 3G was fully to adequately preserved, but missed frames could momentarily decrease the diagnostic value. This research demonstrates that 3G and 802.11 g wireless networks combined with efficient video compression make diagnostically valuable wireless streaming of ultrasound video feasible.

Machine learning models for synthesizing actionable care decisions on lower extremity wounds.

Lower extremity chronic wounds affect 4.5 million Americans annually. Due to inadequate access to wound experts in underserved areas, many patients receive non-uniform, non-standard wound care, resulting in increased costs and lower quality of life. We explored machine learning classifiers to generate actionable wound care decisions about four chronic wound types (diabetic foot, pressure, venous, and arterial ulcers). These decisions (target classes) were: (1) Continue current treatment, (2) Request non-urgent change in treatment from a wound specialist, (3) Refer patient to a wound specialist. We compare classification methods (single classifiers, bagged & boosted ensembles, and a deep learning network) to investigate (1) whether visual wound features are sufficient for generating a decision and (2) whether adding unstructured text from wound experts increases classifier accuracy. Using 205 wound images, the Gradient Boosted Machine (XGBoost) outperformed other methods when using both visual and textual wound features, achieving 81% accuracy. Using only visual features decreased the accuracy to 76%, achieved by a Support Vector Machine classifier. We conclude that machine learning classifiers can generate accurate wound care decisions on lower extremity chronic wounds, an important step toward objective, standardized wound care. Higher decision-making accuracy was achieved by leveraging clinical comments from wound experts.

Urolithiasis and cystitis associated with Staphylococcus delphini group A and mortality in post-weaning mink kits (Neovison vison).

Mortality of mink kits represents a significant loss to production. However, causes of post-weaning mortality in mink kits in modern Danish mink production systems are still relatively poorly documented. We performed a cross-sectional mortality study on eight Danish mink farms including 1893 post mortem examinations of mink kits found dead or euthanized. We assessed the prevalence of cystitis and urolithiasis leading to mortality. Gross pathological findings as well as animal characteristics were recorded and associations with post mortem microbiology (using culture and MaldiTof-MS Vitek MS system) were investigated. Cystitis and/or urolithiasis were associated with death in 33 % (n = 476) and 37 % (n = 166) of the examined mink kits in 2015 and 2017. On farm level, the prevalence of cystitis and/or urolithiasis leading to mortality varied from 0.25 % to 1.27 % with a low overall mortality of 0.9-4.5 %. The bacterial agent most frequently isolated in post mortem bladder swabs from mink with a post mortem diagnosis of urolithiasis and cystitis was Staphylococcus delphini group A (51/283) with a significant (p < 0.0001, CI = [19.5;4745.7]) association to gross pathological findings in the urinary tract. Staphylococcus delphini group A was cultured from 70 % of the skin swabs obtained from apparently healthy mink euthanized at pelting (n = 222). In conclusion urinary tract disease (cystitis and urolithiasis) was the most prevalent post mortem diagnosis during the growth period and was associated with Staphylococcus delphini group A.

Do Novice and Expert Users of Clinical Decision Support Tools Need Different Explanations?

A key requirement for the successful adoption of clinical decision support systems (CDSS) is their ability to provide users with reliable explanations for any given recommendation which can be challenging for some tasks such as wound management decisions. Despite the abundance of decision guidelines, wound non-expert (novice hereafter) clinicians who usually provide most of the treatments still have decision uncertainties. Our goal is to evaluate the use of a Wound CDSS smartphone App that provides explanations for recommendations it produces. The App utilizes wound images taken by the novice clinician using smartphone camera. This study experiments with two proposed variations of rule-tracing explanations called verbose-based and gist-based. Deriving upon theories of decision making, and unlike prior literature that says rule-tracing explanations are only preferred by novices, we hypothesize that, rule-tracing explanations are preferred by both clinicians but in different forms: novices prefer verbose-based rule-tracing and experts prefer gist-based rule-tracing.

Semantic Segmentation of Smartphone Wound Images: Comparative Analysis of AHRF and CNN-Based Approaches.

Smartphone wound image analysis has recently emerged as a viable way to assess healing progress and provide actionable feedback to patients and caregivers between hospital appointments. Segmentation is a key image analysis step, after which attributes of the wound segment (e.g. wound area and tissue composition) can be analyzed. The Associated Hierarchical Random Field (AHRF) formulates the image segmentation problem as a graph optimization problem. Handcrafted features are extracted, which are then classified using machine learning classifiers. More recently deep learning approaches have emerged and demonstrated superior performance for a wide range of image analysis tasks. FCN, U-Net and DeepLabV3 are Convolutional Neural Networks used for semantic segmentation. While in separate experiments each of these methods have shown promising results, no prior work has comprehensively and systematically compared the approaches on the same large wound image dataset, or more generally compared deep learning vs non-deep learning wound image segmentation approaches. In this paper, we compare the segmentation performance of AHRF and CNN approaches (FCN, U-Net, DeepLabV3) using various metrics including segmentation accuracy (dice score), inference time, amount of training data required and performance on diverse wound sizes and tissue types. Improvements possible using various image pre- and post-processing techniques are also explored. As access to adequate medical images/data is a common constraint, we explore the sensitivity of the approaches to the size of the wound dataset. We found that for small datasets (< 300 images), AHRF is more accurate than U-Net but not as accurate as FCN and DeepLabV3. AHRF is also over 1000x slower. For larger datasets (> 300 images), AHRF saturates quickly, and all CNN approaches (FCN, U-Net and DeepLabV3) are significantly more accurate than AHRF.

Multi-channel peristaltic pump for microfluidic applications featuring monolithic PDMS inlay.

The design, fabrication and characterization of a miniaturized, mechanically-actuated 12-channel peristaltic pump for microfluidic applications and built from simple, low-cost materials and fabrication methods is presented. Two pump configurations are tested, including one which reduces pulsating flow. Both use a monolithic PDMS pumping inlay featuring three-dimensional geometries favourable to pumping applications and 12 wholly integrated circular channels. Flow rates in the sub-microL min(-1) to microL min(-1) range were obtained. Channel-to-channel flow rate variability was comparable to a commercial pumping system at lower flow rates. The small footprint, 40 mm by 80 mm, of the micropump renders it portable, and allows its use on microscope stages adjacent to microfluidic devices, thus reducing system dead volumes. The micropump's design allows potential use in remote and resource-limited locations.

Material limitations on the detection limit in refractometry.

We discuss the detection limit for refractometric sensors relying on high-Q optical cavities and show that the ultimate classical detection limit is given by min {Δn} ≳ η, with n + iη being the complex refractive index of the material under refractometric investigation. Taking finite Q factors and filling fractions into account, the detection limit declines. As an example we discuss the fundamental limits of silicon-based high-Q resonators, such as photonic crystal resonators, for sensing in a bio-liquid environment, such as a water buffer. In the transparency window (λ ≳ 1100 nm) of silicon the detection limit becomes almost independent on the filling fraction, while in the visible, the detection limit depends strongly on the filling fraction because the silicon absorbs strongly.

Boundary determination of foot ulcer images by applying the associative hierarchical random field framework.

As traditional visual-examination-based methods provide neither reliable nor consistent wound assessment, several computer-based approaches for quantitative wound image analysis have been proposed in recent years. However, these methods require either some level of human interaction for proper image processing or that images be captured under controlled conditions. However, to become a practical tool of diabetic patients for wound management, the wound image algorithm needs to be able to correctly locate and detect the wound boundary of images acquired under less-constrained conditions, where the illumination and camera angle can vary within reasonable bounds. We present a wound boundary determination method that is robust to lighting and camera orientation perturbations by applying the associative hierarchical random field (AHRF) framework, which is an improved conditional random field (CRF) model originally applied to natural image multiscale analysis. To validate the robustness of the AHRF framework for wound boundary recognition tasks, we have tested the method on two image datasets: (1) foot and leg ulcer images (for the patients we have tracked for 2 years) that were captured under one of the two conditions, such that 70% of the entire dataset are captured with image capture box to ensure consistent lighting and range and the remaining 30% of the images are captured by a handheld camera under varied conditions of lighting, incident angle, and range and (2) moulage wound images that were captured under similarly varied conditions. Compared to other CRF-based machine learning strategies, our new method provides a determination accuracy with the best global performance rates (specificity: > 95 % and sensitivity: > 77 % .

Labour force activity after 65: what explain recent trends in Denmark, Germany and Sweden?

In most OECD member countries labour force attachment, has increased in recent years not only in the age groups 60-64 years but also among people 65 years and older. Focus in this paper is on the trend in older workers' labour force participation in Denmark, Germany and Sweden since 2004. Main emphasis is given to people aged 65-69 years eligible for social security retirement programs from age 65. The gender aspect is included to accommodate different trends for women and men. To explain country differences in trends, the importance of changes in retirement policies of relevance for this age group and cohort relevant changes in education and health is examined and discussed. Further, country differences in the impact from education and health is examined. Results show that the largest increase in labour force participation among people aged 65-69 years has taken place in Sweden following by Germany, while the increase in Denmark is rather small. While the increase in Germany mainly seems to be a result of policy reforms, the increase in Sweden appear to be a result of a combination of policy changes and an increasing educational level. Financial incentives seem most important in Germany and only of minor importance in Denmark, where policy changes directed towards individuals above the age of 65 appear to have been too small so far to affect retirement behaviour significantly.

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification.

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

Absolute measurement of integrated backscatter from arterial wall structures.

Studies done on carotid arteries suggest that the morphology and composition of atherosclerotic plaque are predictive of stroke risk. The goal of this investigation has been to demonstrate that the true acoustic integrated backscatter (IBS) from plaque regions can be measured non-invasively, based on which plaque composition may be inferred and thus become a tool to estimate the likelihood of a lesion or plaque being stable or vulnerable, i.e. having a risk of causing a stroke.To obtain the true IBS non-invasively, the scattering and aberrating effect of the intervening tissue layers must be overcome. This is achieved by using the IBS from arterial blood as a reference backscatter, specifically the backscatter from a blood volume along the same scan line as and adjacent to the region of interest. We have shown that the variance of the IBS estimate of the blood backscatter signal can be quantified and reduced to a specified tolerable level.

A Markov random field approach to group-wise registration/mosaicing with application to ultrasound.

In this paper we present a group-wise non-rigid registration/mosaicing algorithm based on block-matching, which is developed within a probabilistic framework. The discrete form of its energy functional is linked to a Markov Random Field (MRF) containing double and triple cliques, which can be effectively optimized using modern MRF optimization algorithms popular in computer vision. Also, the registration problem is simplified by introducing a mosaicing function which partitions the composite volume into regions filled with data from unique, partially overlapping source volumes. Ultrasound confidence maps are incorporated into the registration framework in order to give accurate results in the presence of image artifacts. The algorithm is initially tested on simulated images where shadows have been generated. Also, validation results for the group-wise registration algorithm using real ultrasound data from an abdominal phantom are presented. Finally, composite obstetrics image volumes are constructed using clinical scans of pregnant subjects, where fetal movement makes registration/mosaicing especially difficult. In addition, results are presented suggesting that a fusion approach to MRF registration can produce accurate displacement fields much faster than standard approaches.