Search

Objective The purpose of the present research is to establish measurement equivalence and test differences in reliability between computerized and pencil-and-paper-based tests of spatial cognition. Background Researchers have increasingly adopted computerized test formats, but few attempt to establish equivalence for computer-based and paper-based tests. The mixed results in the literature on the test mode effect, which occurs when performance differs as a function of test medium, highlight the need to test for, instead of assume, measurement equivalence. One domain that has been increasingly computerized and is thus in need of tests of measurement equivalence across test mode is spatial cognition. Method In the present study, 244 undergraduate students completed two measures of spatial ability (i.e., spatial visualization and cross-sectioning) in either computer- or paper-and-pencil-based format. Results Measurement equivalence was not supported across computer-based and paper-based formats for either spatial test. The results also indicated that test administration type affected the types of errors made on the spatial visualization task, which further highlights the conceptual differences between test mediums. Paper-based tests also demonstrated increased reliability when compared with computerized versions of the tests. Conclusion The results of the measurement equivalence tests caution against treating computer- and paper-based versions of spatial measures as equivalent. We encourage subsequent work to demonstrate test mode equivalence prior to the utilization of spatial measures because current evidence suggests they may not reliably capture the same construct. Application The assessment of test type differences may influence the medium in which spatial cognition tests are administered.

BACKGROUND: Digital models are considered the new standard in the clinical practice of orthodontics. However, they are difficult to use in the absence of a true scale for chairside use during clinical appointments. Therefore, an easy standardized method with true-scale paper print images would be of great utility. The aim of this study was to correlate and compare measurements obtained manually in true-scale paper printed images of digital models with those obtained digitally in the computer. METHODS: Sixty desktop scanned digital models (30 upper and 30 lower) were analyzed. Ortho Insight 3D software was used for generating the printed images on paper and for measuring the digital models. Intercanine width, interpremolar width (first and second), intermolar width (first), and arch length measures were performed with both methods and were correlated and compared. RESULTS: There was a high correlation between measurements performed with both methods. Values obtained from paper print images were lower and showed statistically significant differences (

Wearable technology provides number of opportunities in today’s world that trigged all the imaginations for the people. In today’s era we are more and more dependent on computers and to be compatible with the technology we have to be omnipresent and embrace new developments. Our daily use material is imposed by factor such as battery life, processor power, display brightness, network coverage and form factor have led to the delay in the widespread introduction of wearable computers. In this paper we will review wearable early design that was merely used for aircraft and military purpose and compare that with its today’s application that how nowadays they are used for personal activities. It also highlights the scope and market of wearable computers and how it is emerging day by day.

Four experiments were conducted to test whether recent developments in display technology would suffice to eliminate the well-known disadvantages in reading from screen as compared with paper. Proofreading speed and performance were equal for a TFT-LCD and a paper display, but there were more symptoms of eyestrain in the screen condition accompanied by a strong preference for paper (Experiment 1). These results were replicated using a longer reading duration (Experiment 2). Additional experiments were conducted to test hypotheses about the reasons for the higher amount of eyestrain associated with reading from screen. Reduced screen luminance did not change the pattern of results (Experiment 3), but positioning both displays in equal inclination angles eliminated the differences in eyestrain symptoms and increased proofreading speed in the screen condition (Experiment 4). A paper-like positioning of TFT-LCDs seems to enable unimpaired reading without evidence of increased physical strain. Practitioner Summary: Given the developments in screen technology, a re-assessment of the differences in proofreading speed and performance, well-being, and preference between computer screen and paper was conducted. State-of-the-art TFT-LCDs enable unimpaired reading, but a book-like positioning of screens seems necessary to minimise eyestrain symptoms.

The present comment concerns some doubtful results included in the above paper.

Opportunities for clinicians, researchers, and medical students to become acquainted with the three‐dimensional (3D) anatomy of the human embryo have historically been limited. This work was aimed at creating a collection of digital, printable 3D surface models demonstrating major morphogenetic changes in the embryo's external anatomy, including typical features used for external staging. Twelve models were digitally reconstructed based on optical projection tomography, high‐resolution episcopic microscopy and magnetic resonance imaging datasets of formalin‐fixed specimens of embryos of developmental stages 12 through 23, that is, stages following longitudinal and transverse embryo folding. The reconstructed replica reproduced the external anatomy of the actual specimens in great detail, and the progress of development over stages was recognizable in a variety of external anatomical features and bodily structures, including the general layout and curvature of the body, the pharyngeal arches and cervical sinus, the physiological gut herniation, and external genitalia. In addition, surface anatomy features commonly used for embryo staging, such as distinct steps in the morphogenesis of facial primordia and limb buds, were also apparent. These digital replica, which are all provided for 3D visualization and printing, can serve as a novel resource for teaching and learning embryology and may contribute to a better appreciation of the human embryonic development., This work produced digital three‐dimensional (3D) surface reconstructions of human embryo specimens of stages 12 through 23. The digital replica reproduced the external anatomy of the actual specimens in great detail, and the progress of development over stages was recognizable in a variety of external anatomical features and bodily structures. Reconstructed models are provided for 3D visualization and printing as supplementary files.

A reconfigurable hearing aid is a generic type that can be used for various hearing disabilities without modifying the device hardware. This requires several trials to identify the best matching with the impaired person’s audiogram. The objective of this paper is to propose a novel reconfigurable hearing aid of low complexity with auto-adapting capability which makes it suitable for different types of hearing disabilities ranging from mild to severe intensities. The audio spectrum is divided into three regions and for each region, four different schemes are proposed. An automatic selection of the optimum scheme is proposed for all the regions based on hearing thresholds. Octave and fractional interpolation techniques are performed on a Parks-McClellan based prototype filter to generate the various sub-bands in the reconfigurable filter bank structure. The proposed structure uses only 18 coefficient multipliers which save up to 92% of multipliers when compared to other designs. The delay and matching errors are within the globally accepted limits. The hardware implementation executed on Xilinx Kintex-7 FPGA development board has reaffirmed that the structure is compact and power-efficient. The proposed auto-reconfigurable structure can be used for various types of hearing impairments and can avoid the manual interventions for the selection of schemes in audiogram matching. This in turn minimizes the time to establish the best match with the audiogram. Since the proposed structure has minimal complexity, cost-effective implementation of the device is also possible.

Motivation Genomic data repositories like The Cancer Genome Atlas, Encyclopedia of DNA Elements, Bioconductor’s AnnotationHub and ExperimentHub etc., provide public access to large amounts of genomic data as flat files. Researchers often download a subset of data files from these repositories to perform exploratory data analysis. We developed Epiviz File Server, a Python library that implements an in situ data query system for local or remotely hosted indexed genomic files, not only for visualization but also data transformation. The File Server library decouples data retrieval and transformation from specific visualization and analysis tools and provides an abstract interface to define computations independent of the location, format or structure of the file. We demonstrate the File Server in two use cases: (i) integration with Galaxy workflows and (ii) using Epiviz to create a custom genome browser from the Epigenome Roadmap dataset. Availability and implementation Epiviz File Server is open source and is available on GitHub at http://github.com/epiviz/epivizFileServer. The documentation for the File Server library is available at http://epivizfileserver.rtfd.io.

MotivationRecombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified.ResultsWe have discovered that global structural flexibility, which can be modeled by normalised B-factors, accurately predicts the solubility of 12,216 recombinant proteins expressed in Escherichia coli. We have optimised B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted Index’ (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed ‘SoDoPE’ (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximising both protein expression and solubility.AvailabilityThe SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper2020.

Artificial intelligence (AI) and machine learning algorithms are changing many facets of our lives and have great potential for disease diagnosis. In this Plenary Paper, Matek et al describe training a convolutional neural network model using 171 374 bone marrow cytology images from 945 patients with various hematological diseases before validating its accuracy in classifying single cells in an independent set of 627 images. The system can automatically classify 22 classes of bone marrow leukocytes, and with further improvements, it may bring AI-aided diagnosis of bone marrow biopsy specimens closer to fruition., Key Points A data set of >170 000 microscopic images allows training neural networks for identification of BM cells with high accuracy.Neural networks outperform a feature-based approach to BM cell classification and can be analyzed with explainability and feature embedding methods., Visual Abstract, Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence–based approaches to BM cytomorphology.

Background The study of doctor-patient-computer interactions is a key research area for examining doctor-patient relationships; however, studying these interactions is costly and obtrusive as researchers usually set up complex mechanisms or intrude on consultations to collect, then manually analyze the data. Objective We aimed to facilitate human-computer and human-human interaction research in clinics by providing a computational ethnography tool: an unobtrusive automatic classifier of screen gaze and dialogue combinations in doctor-patient-computer interactions. Methods The classifier’s input is video taken by doctors using their computers' internal camera and microphone. By estimating the key points of the doctor's face and the presence of voice activity, we estimate the type of interaction that is taking place. The classification output of each video segment is 1 of 4 interaction classes: (1) screen gaze and dialogue, wherein the doctor is gazing at the computer screen while conversing with the patient; (2) dialogue, wherein the doctor is gazing away from the computer screen while conversing with the patient; (3) screen gaze, wherein the doctor is gazing at the computer screen without conversing with the patient; and (4) other, wherein no screen gaze or dialogue are detected. We evaluated the classifier using 30 minutes of video provided by 5 doctors simulating consultations in their clinics both in semi- and fully inclusive layouts. Results The classifier achieved an overall accuracy of 0.83, a performance similar to that of a human coder. Similar to the human coder, the classifier was more accurate in fully inclusive layouts than in semi-inclusive layouts. Conclusions The proposed classifier can be used by researchers, care providers, designers, medical educators, and others who are interested in exploring and answering questions related to screen gaze and dialogue in doctor-patient-computer interactions.

Background: Work stress affects individual health and well-being. These negative effects could be mitigated through regular monitoring of employees’ stress. Such monitoring becomes even more important as the digital transformation of the economy implies profound changes in working conditions. Objective: The goal of this study was to investigate the association between computer mouse movements and work stress in the field. Methods: We hypothesized that stress is associated with a speed-accuracy trade-off in computer mouse movements. To test this hypothesis, we conducted a longitudinal field study at a large business organization, where computer mouse movements from regular work activities were monitored over 7 weeks; the study included 70 subjects and 1829 observations. A Bayesian regression model was used to estimate whether self-reported acute work stress was associated with a speed-accuracy trade-off in computer mouse movements. Results: There was a negative association between stress and the two-way interaction term of mouse speed and accuracy (mean −0.32, 95% highest posterior density interval −0.58 to −0.08), which means that stress was associated with a speed-accuracy trade-off. The estimated association was not sensitive to different processing of the data and remained negative after controlling for the demographics, health, and personality traits of subjects. Conclusions: Self-reported acute stress is associated with computer mouse movements, specifically in the form of a speed-accuracy trade-off. This finding suggests that the regular analysis of computer mouse movements could indicate work stress., Journal of Medical Internet Research, 23 (4), ISSN:1438-8871

Background and Objective: Nowadays usage paradigms of medical imaging resources are requesting vendor-neutral archives, accessible through standard interfaces, with multi-repository support. Regional repositories shared by distinct institutions, teleradiology as a service at Cloud, teaching and research archives, are illustrative examples of this new reality. However, traditional production environments have a server archive instance per functional domain where every registered client application has access to all studies. This paper proposes an innovator ownership concept and access control mechanisms that provide a multi-repository environment and integrates well with standard protocols. Methods: A secure accounting mechanism for medical imaging repositories were designed and instantiated as an extension of a well-known open-source archive. A new Web services layer was implemented to provide a vendor-neutral solution complaint with modern DICOM-Web protocols for storage, search and retrieve of medical imaging data. Results: The concept validation was done through the integration of proposed architecture in an open-source solution. A quantitative assessment was performed for evaluating the impact of the mechanism in the usual DICOM Web operations. Conclusions: This article proposes a secure accounting architecture able to easily convert a standard medical imaging archive server in a multi-repository solution. The proposal validation was done through a set of tests that demonstrated its robustness and usage feasibility in a production environment. The proposed system offers new services, fundamental in a new era of Cloud-based operations, with acceptable temporal costs., 9 pages, 7 figures and 2 tables. Journal submission

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.

Motivation Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. Results Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. Availabilityand implementation Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. Supplementary information Supplementary data are available at Bioinformatics online.

The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC)-based methods. Compared to multilayered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. We report a highly efficient MMC—MMCL—using the OpenCL heterogeneous computing framework and demonstrate a speedup ratio up to 420× compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay, and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.

Ready access to relevant real-time information in medical imaging offers several potential benefits. Knowing both when important information will be available and that important information is available can facilitate optimization of workflow and management of time. Unexpected findings, as well as deficiencies in reporting and documentation, can be immediately managed. Herein, we present our experience developing and implementing a real-time web-centric dashboard system for radiologists, clinicians, and support staff. The dashboards are driven by multi-sourced HL7 message streams that are monitored, analyzed, aggregated, and transformed into multiple real-time displays to improve operations within our department. We call this framework Pipeline. Ruby on Rails, JavaScript, HTML, and SQL serve as the foundations of the Pipeline application. HL7 messages are processed in real-time by a Mirth interface engine which posts exam data into SQL. Users utilize web browsers to visit the Ruby on Rails–based dashboards on any device connected to our hospital network. The dashboards will automatically refresh every 30 seconds using JavaScript. The Pipeline application has been well received by clinicians and radiologists.

Background Prolonged time of computer use increases the prevalence of ocular problems, including eye strain, tired eyes, irritation, redness, blurred vision, and double vision, which are collectively referred to as computer vision syndrome (CVS). Approximately 70% of computer users have vision-related problems. For these reasons, properly designed interventions for users with CVS are required. To design an effective screen intervention for preventing or improving CVS, we must understand the effective interfaces of computer-based interventions. Objective In this study, we aimed to explore the interface elements of computer-based interventions for CVS to set design guidelines based on the pros and cons of each interface element. Methods We conducted an iterative user study to achieve our research objective. First, we conducted a workshop to evaluate the overall interface elements that were included in previous systems for CVS (n=7). Through the workshop, participants evaluated existing interface elements. Based on the evaluation results, we eliminated the elements that negatively affect intervention outcomes. Second, we designed our prototype system LiquidEye that includes multiple interface options (n=11). Interface options included interface elements that were positively evaluated in the workshop study. Lastly, we deployed LiquidEye in the real world to see how the included elements affected the intervention outcomes. Participants used LiquidEye for 14 days, and during this period, we collected participants’ daily logs (n=680). Additionally, we conducted prestudy and poststudy surveys, and poststudy interviews to explore how each interface element affects participation in the system. Results User data logs collected from the 14 days of deployment were analyzed with multiple regression analysis to explore the interface elements affecting user participation in the intervention (LiquidEye). Statistically significant elements were the instruction page of the eye resting strategy (P=.01), goal setting of the resting period (P=.009), compliment feedback after completing resting (P Conclusions Based on the study results, we suggested design implications to consider when designing computer-based interventions for CVS. The sophisticated design of the customization interface can make it possible for users to use the system more interactively, which can result in higher engagement in managing eye conditions. There are important technical challenges that still need to be addressed, but given the fact that this study was able to clarify the various factors related to computer-based interventions, the findings are expected to contribute greatly to the research of various computer-based intervention designs in the future.

Background: Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease and one of the most common forms of movement disorder. Although there is no known cure for PD, existing therapies can provide effective symptomatic relief. However, optimal titration is crucial to avoid adverse effects. Today, decision making for PD management is challenging because it relies on subjective clinical evaluations that require a visit to the clinic. This challenge has motivated recent research initiatives to develop tools that can be used by nonspecialists to assess psychomotor impairment. Among these emerging solutions, we recently reported the neuroQWERTY index, a new digital marker able to detect motor impairment in an early PD cohort through the analysis of the key press and release timing data collected during a controlled in-clinic typing task. Objective: The aim of this study was to extend the in-clinic implementation to an at-home implementation by validating the applicability of the neuroQWERTY approach in an uncontrolled at-home setting, using the typing data from subjects’ natural interaction with their laptop to enable remote and unobtrusive assessment of PD signs. Methods: We implemented the data-collection platform and software to enable access and storage of the typing data generated by users while using their computer at home. We recruited a total of 60 participants; of these participants 52 (25 people with Parkinson’s and 27 healthy controls) provided enough data to complete the analysis. Finally, to evaluate whether our in-clinic-built algorithm could be used in an uncontrolled at-home setting, we compared its performance on the data collected during the controlled typing task in the clinic and the results of our method using the data passively collected at home. Results: Despite the randomness and sparsity introduced by the uncontrolled setting, our algorithm performed nearly as well in the at-home data (area under the receiver operating characteristic curve [AUC] of 0.76 and sensitivity/specificity of 0.73/0.69) as it did when used to evaluate the in-clinic data (AUC 0.83 and sensitivity/specificity of 0.77/0.72). Moreover, the keystroke metrics presented a strong correlation between the 2 typing settings, which suggests a minimal influence of the in-clinic typing task in users’ normal typing. Conclusions: The finding that an algorithm trained on data from an in-clinic setting has comparable performance with that tested on data collected through naturalistic at-home computer use reinforces the hypothesis that subtle differences in motor function can be detected from typing behavior. This work represents another step toward an objective, user-convenient, and quasi-continuous monitoring tool for PD.