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In children, vital distress events, particularly respiratory, go unrecognized. To develop a standard model for automated assessment of vital distress in children, we aimed to construct a prospective high-quality video database for critically ill children in a pediatric intensive care unit (PICU) setting. The videos were acquired automatically through a secure web application with an application programming interface (API). The purpose of this article is to describe the data acquisition process from each PICU room to the research electronic database. Using an Azure Kinect DK and a Flir Lepton 3.5 LWIR attached to a Jetson Xavier NX board and the network architecture of our PICU, we have implemented an ongoing high-fidelity prospectively collected video database for research, monitoring, and diagnostic purposes. This infrastructure offers the opportunity to develop algorithms (including computational models) to quantify vital distress in order to evaluate vital distress events. More than 290 RGB, thermographic, and point cloud videos of each 30 s have been recorded in the database. Each recording is linked to the patient's numerical phenotype, i.e., the electronic medical health record and high-resolution medical database of our research center. The ultimate goal is to develop and validate algorithms to detect vital distress in real time, both for inpatient care and outpatient management. [ABSTRACT FROM AUTHOR]

This article describes a new approach for quantitative evaluation of respiration in the pediatric intensive care unit (PICU). Video sequences of thorax movements are recorded by two depth cameras to cover the 3-D surface of the torso and its lateral sides. The breathing activity implies a frame-by-frame surface deformation, which can be described by the volume variation of reconstructed surfaces between the consecutive video frames. A quantitative evaluation of the breathing pattern is then performed through a subtraction technique, thereby detecting the volume variation between the subsequent frames. A high-fidelity simulation was performed in a realistic environment designed for critically ill children. The simulation was then followed by a real-world evaluation, involving two newborn babies (one female and one male) who required ventilator support. The breathing signal patterns resulting from our approach were compared to those measured by mechanical ventilation in terms of their waveforms, evaluating the most significant dynamic parameters: tidal volume, respiratory rate, and minute ventilation. Our experimental study showed a significant agreement between the proposed 3-D imaging system and the gold standard method in estimating respiratory waveforms and parameters. This article presents the following innovations. First, we suggest a 3-D imaging system specifically designed for PICUs based on a contactless design. Second, we propose an efficient positioning mechanism for the cameras, offering a very high spatial coverage of the thoracoabdominal zone and considering the PICU constraints. Finally, we propose an objective vision-based method to quantitatively measure respiration for spontaneous breathing patients in PICUs. [ABSTRACT FROM AUTHOR]

Byline: Rita Noumeir Integrating the healthcare enterprise (IHE) is an initiative where healthcare professionals and industry providers work together to improve the way healthcare computer systems share information and achieve a high level of interoperability. With IHE, the care providers identify the needs for integration, and manufacturers implement solutions, providing systems that communicate better, are easier to deploy and less expensive to maintain. Successfully implemented in such clinical domains as radiology, laboratory services and cardiology, IHE is expanding into new domains, such as ophthalmology and medical devices, and solving integration problems common to all clinical domains, including patient management, order management, registry, security and document sharing. In this paper, we describe and analyse the collaborative and rigorous IHE process that delivers faster, more efficient and cost-effective systems integration and show how IHE can help healthcare policymakers, executives, and professionals to achieve optimal patient care.

Background: In an intensive care units, experts in mechanical ventilation are not continuously at patient’s bedside to adjust ventilation settings and to analyze the impact of these adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. Objective: The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict transcutaneous hemoglobin oxygen saturation 5 min (5min SpO2) after a ventilator setting change. Data sources: Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 776,727 data rows were obtained from 610 patients, discretized into 3 class labels (< 84%, 85% to 91% and c92% to 100%). Performance metrics of predictive models: Due to data imbalance, four different data balancing processes were applied. Then, two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with area under the curves < 0.75. Conclusion: This single center pilot study using machine learning predictive model resulted in an algorithm with poor accuracy. The comparison of machine learning models showed that bagged complex trees was a promising approach. However, there is a need to improve these models before incorporating them into a clinical decision support systems. One potentially solution for improving predictive model, would be to increase the amount of data available to limit over-fitting that is potentially one of the cause for poor classification performances for 2 of the three class labels. [ABSTRACT FROM AUTHOR]

Higher plants are an integral part of strategies for sustained human presence in space. Space-based greenhouses have the potential to provide closed-loop recycling of oxygen, water and food. Plant monitoring systems with the capacity to remotely observe the condition of crops in real-time within these systems would permit operators to take immediate action to ensure optimum system yield and reliability. One such plant health monitoring technique involves the use of reporter genes driving fluorescent proteins as biological sensors of plant stress. In 2006 an initial prototype green fluorescent protein imager system was deployed at the Arthur Clarke Mars Greenhouse located in the Canadian High Arctic. This prototype demonstrated the advantageous of this biosensor technology and underscored the challenges in collecting and managing telemetric data from exigent environments. We present here the design and deployment of a second prototype imaging system deployed within and connected to the infrastructure of the Arthur Clarke Mars Greenhouse. This is the first imager to run autonomously for one year in the un-crewed greenhouse with command and control conducted through the greenhouse satellite control system. Images were saved locally in high resolution and sent telemetrically in low resolution. Imager hardware is described, including the custom designed LED growth light and fluorescent excitation light boards, filters, data acquisition and control system, and basic sensing and environmental control. Several critical lessons learned related to the hardware of small plant growth payloads are also elaborated. [ABSTRACT FROM AUTHOR]

Background: With the deployments of Electronic Health Records (EHR), interoperability testing in healthcare is becoming crucial. EHR enables access to prior diagnostic information in order to assist in health decisions. It is a virtual system that results from the cooperation of several heterogeneous distributed systems. Interoperability between peers is therefore essential. Achieving interoperability requires various types of testing. Implementations need to be tested using software that simulates communication partners, and that provides test data and test plans. Results: In this paper we describe a software that is used to test systems that are involved in sharing medical images within the EHR. Our software is used as part of the Integrating the Healthcare Enterprise (IHE) testing process to test the Cross Enterprise Document Sharing for imaging (XDS-I) integration profile. We describe its architecture and functionalities; we also expose the challenges encountered and discuss the elected design solutions. Conclusions: EHR is being deployed in several countries. The EHR infrastructure will be continuously evolving to embrace advances in the information technology domain. Our software is built on a web framework to allow for an easy evolution with web technology. The testing software is publicly available; it can be used by system implementers to test their implementations. It can also be used by site integrators to verify and test the interoperability of systems, or by developers to understand specifications ambiguities, or to resolve implementations difficulties. [ABSTRACT FROM AUTHOR]

The Digital Imaging and Communications in Medicine (DICOM) standard recently introduced rules for the encoding, transmission and storage of the structured diagnostic report. This medical document can be stored and communicated with the images. It has a structure and may contain text, with links to other data such as images, waveforms and spatial or temporal coordinates. The prompt availability of the radiology report is crucial for implementing a Patient Electronic Record. Using the extensible markup language (XML) to encode the radiology report broadens its accessibly. This paper presents a document type definition (DTD) that accurately models the DICOM structured report content, structure, and constraints. [ABSTRACT FROM AUTHOR]

Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments. [ABSTRACT FROM AUTHOR]

In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to issues such as occlusion and often need substantial computational resources. In this work, we address human action recognition through inertial sensor signals, which have a vast quantity of practical applications in fields such as sports analysis and human-machine interfaces. For that purpose, we propose a new learning framework built around a 1D-CNN architecture, which we validated by achieving very competitive results on the publicly available UTD-MHAD dataset. Moreover, the proposed method provides some answers to two of the greatest challenges currently faced by action recognition algorithms, which are (1) the recognition of high-level activities and (2) the reduction of their computational cost in order to make them accessible to embedded devices. Finally, this paper also investigates the tractability of the features throughout the proposed framework, both in time and duration, as we believe it could play an important role in future works in order to make the solution more intelligible, hardware-friendly and accurate. [ABSTRACT FROM AUTHOR]

Learn how the Cross-Enterprise Document Sharing (XDS) integration profile helped provide the interoperability necessary to deploy electronic health records, giving healthcare providers access to medical records regardless of location or institution. [ABSTRACT FROM AUTHOR]

According to ADQI criteria, AKI was defined as rapidly reversed in 42 patients (25.3% of AKI patients), persistent AKI in 16 patients (9.7%) and as acute kidney disease (AKD) in 64 patients (38.6%), remaining patients couldn't be classified (n = 44). 108 (61.0%) patients died in intensive care, 76 (43.4%) patients had a hemorrhagic complication, 46 patients (26%) had a severe hemorrhagic complication defined by death or/and massive transfusion or/and hemostatic procedure (surgery or endoscopy), 87 (49.1%) patients had thrombopenia < 100 G/L, 50 (28.2%) patients had thrombopenia < 50 G/L, 53 patients (30.1%) and 43 patients (24.6%) required antiaggregation and anticoagulation withdrawal. Tracheal disorders patterns were: tracheal ring fractures (15 patients), granulomas (11 patients), tracheomalacia (7 patients), subglottic edema (4 patients), tracheal stenosis (2 patients). Concerning the other 31 patients, antibiotics prescribed were: amoxicillin/clavulanic acid in 17 patients (47.2%), carbapenems in 17 patients (47.2%), aminosids in 13 patients (36.1%), glycopeptides in 10 patients (27.8%), colistin in 9 patients (25%), fluoroquinolones in 7 patients (19.4%), cephalosporins in 6 patients (16.6%), tigecycline in 4 patients (11.1%). For the modalities of BT: the cross match was done for 7 patients in ICU (18% of cases), and for 2 patients of CVS in block A2 (7% of patients), we had 2 anti-B agglutinations (2.34%): one in ICU patient AB+, and one in patient block A+, for each one blood bags have been changed. [Extracted from the article]

This manuscript introduces a novel linear weighted watermarking through normalized principal components using discrete wavelet transform (DWT) and singular value decomposition (SVD). Weight evaluation for embedding a singular value matrix of a watermark into a host is a trivial task in watermark embedding process. This task is accomplished by the normalized principal components derived from the singular value matrices of HH subbands of the host and the watermark images. Experiments are conducted to analyze the effectiveness of the proposed watermark embedding and watermark-host extraction processes against the other DWT-SVD based watermarking schemes. Performance of this method is analyzed by peak signal to noise ratio, structural similarity index and correlation coefficient. The proposed method is also tested against various geometrical and non-geometrical attacks on watermarked images. [ABSTRACT FROM AUTHOR]