Synchronous communication in PLM environments using annotated CAD models

The connection of resources, data, and knowledge through communication technology plays a vital role in current collaborative design methodologies and Product Lifecycle Management (PLM) systems, as these elements act as channels for information and meaning. Despite significant advances in the area of PLM, most communication tools are used as separate services that are disconnected from existing development environments. Consequently, during a communication session, the specific elements being discussed are usually not linked to the context of the discussion, which may result in important information getting lost or becoming difficult to access. In this paper, we present a method to add synchronous communication functionality to a PLM system based on annotated information embedded in the CAD model. This approach provides users a communication channel that is built directly into the CAD interface and is valuable when individuals need to be contacted regarding the annotated aspects of a CAD model. We present the architecture of a new system and its integration with existing PLM systems, and describe the implementation details of an annotation-based video conferencing module for a commercial CAD application.This work was supported by the Spanish Ministry of Economy and Competitiveness and the FEDER Funds, through the ANNOTA project (Ref. TIN2013-46036-C3-1-R).Camba, JD.; Contero, M.; Salvador Herranz, GM.; Plumed, R. (2016). Synchronous communication in PLM environments using annotated CAD models. Journal of Systems Science and Systems Engineering. 25(2):142-158. https://doi.org/10.1007/s11518-016-5305-5S142158252Abrahamson, S., Wallace, D., Senin, N. & Sferro, P. (2000). Integrated design in a service marketplace. Computer-Aided Design, 32(2):97–107.Ahmed, S. (2005). 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Fluid challenges in intensive care: the FENICE study A global inception cohort study

Fluid challenges (FCs) are one of the most commonly used therapies in critically ill patients and represent the cornerstone of hemodynamic management in intensive care units. There are clear benefits and harms from fluid therapy. Limited data on the indication, type, amount and rate of an FC in critically ill patients exist in the literature. The primary aim was to evaluate how physicians conduct FCs in terms of type, volume, and rate of given fluid; the secondary aim was to evaluate variables used to trigger an FC and to compare the proportion of patients receiving further fluid administration based on the response to the FC.This was an observational study conducted in ICUs around the world. Each participating unit entered a maximum of 20 patients with one FC.2213 patients were enrolled and analyzed in the study. The median [interquartile range] amount of fluid given during an FC was 500 ml (500-1000). The median time was 24 min (40-60 min), and the median rate of FC was 1000 [500-1333] ml/h. The main indication for FC was hypotension in 1211 (59 %, CI 57-61 %). In 43 % (CI 41-45 %) of the cases no hemodynamic variable was used. Static markers of preload were used in 785 of 2213 cases (36 %, CI 34-37 %). Dynamic indices of preload responsiveness were used in 483 of 2213 cases (22 %, CI 20-24 %). No safety variable for the FC was used in 72 % (CI 70-74 %) of the cases. There was no statistically significant difference in the proportion of patients who received further fluids after the FC between those with a positive, with an uncertain or with a negatively judged response.The current practice and evaluation of FC in critically ill patients are highly variable. Prediction of fluid responsiveness is not used routinely, safety limits are rarely used, and information from previous failed FCs is not always taken into account

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach

Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests and provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity. With the long term goal to provide new evidence for the diagnosis, this paper presents an evaluation of different methods based on a deep neural network. These are the first steps to develop an automatic COVID-19 diagnosis tool using chest X-Ray images to differentiate between controls, pneumonia, or COVID-19 groups. The paper describes the process followed to train a Convolutional Neural Network with a dataset of more than 79, 500 X-Ray images compiled from different sources, including more than 8, 500 COVID-19 examples. Three different experiments following three preprocessing schemes are carried out to evaluate and compare the developed models. The aim is to evaluate how preprocessing the data affects the results and improves its explainability. Likewise, a critical analysis of different variability issues that might compromise the system and its effects is performed. With the employed methodology, a 91.5% classification accuracy is obtained, with an 87.4% average recall for the worst but most explainable experiment, which requires a previous automatic segmentation of the lung region

Evaluation of wavelet measures on automatic detection of emotion in noisy and telephony speech signals

Detection of emotion in humans from speech signals is a recent research field. One of the scenarios where this field has been applied is in situations where the human integrity and security are at risk. In this paper we are propossing a set of features based on the Teager energy operator, and several entropy measures obtained from the decomposition signals from discrete wavelet transform to characterize different types of negative emotions such as anger, anxiety, disgust, and desperation. The features are measured in three different conditions: (1) the original speech signals, (2) the signals that are contaminated with noise, or are affected by the presence of a phone channel, and (3) the signals that are obtained after processing using an algorithm for Speech Enhancement based on Karhunen-Love Transform. According to the results, when the speech enhancement is applied, the detection of emotion in speech is increased in up to 22% compared to results obtained when the speech signal is highly contaminated with noise. © 2014 IEEE

Interobserver Agreement in Magnetic Resonance of the Sacroiliac Joints in Patients with Spondyloarthritis

Background. Clinical, laboratory, and radiologic parameters are used for diagnosis and classification of spondyloarthritis (SpA). Magnetic resonance imaging (MRI) of sacroiliac (SI) joints is being increasingly used to detect early sacroiliitis. We decided to evaluate the interobserver agreement in MRI findings of SI joints of SpA patients between a local radiologist, a rheumatologist, and an expert radiologist in musculoskeletal diseases. Methods. 66 MRI images of the SI joints of patients with established diagnosis of SpA were evaluated. Agreement was expressed in Cohen’s kappa. Results. Interobserver agreement between a local radiologist and an expert radiologist was fair (κ=0.37). Only acute findings showed a moderate agreement (κ=0.45), while chronic findings revealed 76.5% of disagreement (κ=0.31). A fair agreement was observed in acute findings (κ=0.38) as well as chronic findings (κ=0.38) between a local radiologist and a rheumatologist. There was a substantial agreement between an expert radiologist and a rheumatologist (κ=0.73). In acute findings, a 100% agreement was achieved. Also chronic and acute plus chronic findings showed high levels of agreement (κ=0.73 and 0.62, resp.). Conclusions. Our study shows that rheumatologists may have similar MRI interpretations of SI joints in SpA patients as an expert radiologist