scholarly journals Wheeze and cough measurements at night in children with respiratory symptoms

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Markus Lindenhofer ◽  
Lena Roth ◽  
Clemens Mädel ◽  
Florian Götzinger ◽  
Katharina Kainz ◽  
...  
Keyword(s):  
Respiratory Diseases ◽  
Detection System ◽  
Automated Detection ◽  
New Device ◽  
Additional Information ◽  
Therapeutic Benefits ◽  
Breath Sounds ◽  
Habit Cough ◽  
User Friendly ◽  
Nocturnal Cough

Abstract Background Nocturnal cough and wheeze are important symptoms when diagnosing any respiratory disease in a child, but objective measurements of these symptoms are not performed. Methods The aim of our study was to analyze the use of an automated detection system to assess breath sounds objectively in comparison to cough and wheeze questionnaires and to evaluate its feasibility in clinical practice. Results Forty-nine recordings of thirty-nine children were processed (asthma n = 13; cystic fibrosis n = 2; pneumonia n = 5; suspicion of habit cough n = 7; prolonged, recurrent or chronic cough n = 13), and cough and asthma scores were compared to the objective nocturnal recordings. Time for audio-validation of recordings took between 2 and 40 min (mean: 14.22 min, (SD): 10.72). Accuracy of the automated measurement was higher for cough than for wheezing sounds. Nocturnal cough readings but not wheeze readings correlated with some of the corresponding scores. Conclusion To our knowledge this is the first study using a new device to assess nocturnal cough and obstructive breath sounds objectively in children with a wide variety of respiratory diseases. The assessment proved user friendly. We obtained additional information on nighttime symptoms, which would otherwise have remained obscure. Further studies to assess possible diagnostic and therapeutic benefits of this device are needed.

Deep learning for automated detection and numbering of permanent teeth on panoramic images

2021 ◽  
Author(s):  
Mohamed Estai ◽  
Marc Tennant ◽  
Dieter Gebauer ◽  
Andrew Brostek ◽  
Janardhan Vignarajan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Detection System ◽  
Ground Truth ◽  
Automated Detection ◽  
Permanent Teeth ◽  
Automated Method ◽  
Step Procedure ◽  
Panoramic Images ◽  
Tooth Number

Objective: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). Methods: In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. Results: The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. Conclusion: The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

scholarly journals The Roball

2002 ◽  
Vol 35 (3) ◽  
pp. 204-207 ◽  
Author(s):  
William H. Robinson
Keyword(s):  
Seismic Isolation ◽  
Inverted Pendulum ◽  
Experimental Results ◽  
Flat Plates ◽  
Isolation System ◽  
Friction Forces ◽  
New Device ◽  
System A ◽  
Full Scale Tests ◽  
User Friendly

Robinson Seismic's latest developments in seismic isolation includes a new device, the RoballTM, for seismically isolating structures during earthquakes. This advance is a new concept for seismic isolation based on the principle of the inverted pendulum. It consists of 'friction balls' or 'Roballs' moving between upper and lower spherical like cavities or flat plates. The Roballs are filled with a material which is able to provide the friction forces required to absorb the energy from numerous earthquakes while supporting the structure. The Roball technique is expected to enable light and in the future possibly heavy structures to be more economically seismically isolated. As part of a program to develop a user friendly 'seismic isolation system' a series of full-scale tests have been carried out on a number of possible designs including three approaches for vertical pressures of -1 MPa resulting in coefficients of friction of -0.1 to -0.4. In this paper we present the preliminary experimental results.

scholarly journals The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions

10.2196/27663 ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. e27663
Author(s):  
Sandersan Onie ◽  
Xun Li ◽  
Morgan Liang ◽  
Arcot Sowmya ◽  
Mark Erik Larsen
Keyword(s):  
Computer Vision ◽  
Early Intervention ◽  
Suicide Attempt ◽  
Detection System ◽  
Automated Detection ◽  
Video Data ◽  
Narrative Review ◽  
Closed Circuit ◽  
Closed Circuit Television ◽  
Area Of Interest

Background Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors at metro stations, and using computer vision to identify a suicide attempt in progress. However, there has been no review of suicide research and interventions using CCTV and video. Objective The objective of this study was to review the literature to understand how CCTV and video data have been used in understanding and preventing suicide. Furthermore, to more fully capture progress in the field, we report on an ongoing study to respond to an identified gap in the narrative review, by using a computer vision–based system to identify behaviors prior to a suicide attempt. Methods We conducted a search using the keywords “suicide,” “cctv,” and “video” on PubMed, Inspec, and Web of Science. We included any studies which used CCTV or video footage to understand or prevent suicide. If a study fell into our area of interest, we included it regardless of the quality as our goal was to understand the scope of how CCTV and video had been used rather than quantify any specific effect size, but we noted the shortcomings in their design and analyses when discussing the studies. Results The review found that CCTV and video have primarily been used in 3 ways: (1) to identify risk factors for suicide (eg, inferring depression from facial expressions), (2) understanding suicide after an attempt (eg, forensic applications), and (3) as part of an intervention (eg, using computer vision and automated systems to identify if a suicide attempt is in progress). Furthermore, work in progress demonstrates how we can identify behaviors prior to an attempt at a hotspot, an important gap identified by papers in the literature. Conclusions Thus far, CCTV and video have been used in a wide array of applications, most notably in designing automated detection systems, with the field heading toward an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for preattempt behaviors, which may allow for early intervention.

scholarly journals Remote Blood Pressure Monitoring With a Wearable Photoplethysmographic Device (Senbiosys): Protocol for a Single-Center Prospective Clinical Trial (Preprint)

2021 ◽  
Author(s):  
Sara Schukraft ◽  
Assim Boukhayma ◽  
Stéphane Cook ◽  
Antonino Caizzone
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Prospective Trial ◽  
Vital Signs ◽  
Adult Patients ◽  
Arterial Catheter ◽  
Single Center ◽  
New Device ◽  
Remote Patient ◽  
User Friendly

BACKGROUND Wearable devices can provide user-friendly, accurate, and continuous blood pressure (BP) monitoring to assess patients’ vital signs and achieve remote patient management. Remote BP monitoring can substantially improve BP control. The newest cuffless BP monitoring devices have emerged in patient care using photoplethysmography. OBJECTIVE The Senbiosys trial aims to compare BP measurements of a new device capturing a photoplethysmography signal on the finger versus invasive measurements performed in patients with an arterial catheter in the intensive care unit (ICU) or referred for a coronarography at the Hospital of Fribourg. METHODS The Senbiosys study is a single-center, single-arm, prospective trial. The study population consists of adult patients undergoing coronarography or patients in the ICU with an arterial catheter in place. This study will enroll 35 adult patients, including 25 patients addressed for a coronarography and 10 patients in the ICU. The primary outcome is the assessment of mean bias (95% CI) for systolic BP, diastolic BP, and mean BP between noninvasive and invasive BP measurements. Secondary outcomes include a reliability index (Qualification Index) for BP epochs and count of qualified epochs. RESULTS Patient recruitment started in June 2021. Results are expected to be published by December 2021. CONCLUSIONS The findings of the Senbiosys trial are expected to improve remote BP monitoring. The diagnosis and treatment of hypertension should benefit from these advancements. CLINICALTRIAL ClinicalTrials.gov NCT04379986; https://clinicaltrials.gov/ct2/show/NCT04379986 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/30051

Oxford Handbook of Clinical Pharmacy

2017 ◽  
Keyword(s):  
Clinical Pharmacy ◽  
Neurological Disorders ◽  
Respiratory Diseases ◽  
Clinical Skills ◽  
Chronic Obstructive ◽  
Evidence Based ◽  
Drug Management ◽  
Obstructive Pulmonary Disease ◽  
Controlled Drugs ◽  
User Friendly

Fully updated and revised, the third edition of the Oxford Handbook of Clinical Pharmacy presents practical guidance in a user-friendly format. Supported by prescribing points and linked relevant concepts, it will give you the support and knowledge to practise with confidence. Including key information on controlled drugs, adverse drug reactions and interactions, clinical skills, patient and drug management, as well as therapy-specific issues, this handbook now includes brand new topics covering chronic obstructive pulmonary disease and other respiratory diseases, HIV, tuberculosis, mental health and neurological disorders, health coaching, and applications to formulary committees. With an increased focus on supporting the community pharmacist and designed to complement the British National Formulary guidelines, this handbook is packed with evidence-based advice.

scholarly journals A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Jooyoung Lee ◽  
Jihye Byun ◽  
Jaedeok Lim ◽  
Jaeyun Lee
Keyword(s):  
Detection System ◽  
Automated Detection ◽  
Detection Accuracy ◽  
Occupancy Detection ◽  
Single Side ◽  
Binary Case ◽  
Left And Right ◽  
Hov Lanes ◽  
Labeling Method ◽  
Better Than

High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.

scholarly journals Image Detection System for Schmidt Plates

1984 ◽  
Vol 78 ◽  
pp. 169-171 ◽  
Author(s):  
Hideo Maehara ◽  
Tomohiko Yamagata
Keyword(s):  
Magnetic Tape ◽  
Detection System ◽  
Automated Detection ◽  
The Other ◽  
Image Detection ◽  
Alternative Method ◽  
Intermediate Medium

A 14-inch Schmidt plate contains 109 photographic grains and 105 to 106 images of stars and galaxies on it. Such a quantity of data is too large to be handled in a conventional way even for a big computer.There is, in general, an alternative method to solve this problem; one is to store the data of all pixels on intermediate medium (e.g., magnetic tape), and reduce them into image parameters afterwards. The other method is to do all the processing simultaneously with the measurement. The latter is very useful for the automated detection of celestial images on large Schmidt plates.

Thermal Infrared Polarimetric Sensor for Automated Detection of Oil Spills

2017 ◽  
Vol 2017 (1) ◽  
pp. 2017402
Author(s):  
David B. Chenault ◽  
Justin P. Vaden ◽  
Douglas A. Mitchell ◽  
Erik D. Demicco
Keyword(s):  
Early Detection ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
Thermal Infrared ◽  
Automated Detection ◽  
Unmanned Aerial Systems ◽  
Detection Approach ◽  
Thermal Cameras ◽  
Aerial Systems

One of the most effective ways of minimizing oil spill impact is early detection. Effective early detection requires automated detection that relies as little as possible on an operator and can operate 24/7. A new and innovative optical detection system exploits the polarization of light, the same physics used to reduce glare through the use of polarized glasses but in the thermal infrared (TIR) portion of the optical spectrum. Measuring the polarization of thermally emitted radiation from an oil spill enhances the detection over conventional thermal cameras and has the potential to provide automated day / night monitoring and surveillance. The sensors developed thus far are relatively small and inexpensive and can be easily mounted in areas that need monitoring and installed in unmanned aerial systems (UAS). Since the sensor is adapted from a conventional TIR camera, thermal imagery as currently used is collected in addition to the polarimetric imagery to further improve the detection performance. Lens options enable wide area coverage at shorter ranges and higher resolution at longer ranges from the camera position. A TIR Polarimetric camera was tested at Ohmsett to establish performance under a variety of conditions. The Polarimetric camera was tested during the day and at night, under several different wave conditions generated in the wave tank, and with oil of different compositions and thicknesses. The imagery collected was analyzed to establish the contrast improvement through the polarimetric properties of the oil and to assess the automation of the detection process. In this poster, the sensor and test setup will be briefly described with detailed description of the results and the potential of this detection approach for automated detection.

Table-top Cathodoluminescence Microscopy for Geology

2020 ◽  
Author(s):  
Toon Coenen ◽  
Albert Polman
Keyword(s):  
Electron Beam ◽  
Detection System ◽  
Energetic Electron ◽  
Numerical Aperture ◽  
Graded Index ◽  
Thermo Fisher Scientific ◽  
Deformation Features ◽  
Backscattered Electron ◽  
User Friendly ◽  
Fisher Scientific

<p>Cathodoluminescence (CL) microscopy is a well-known technique for imaging geological specimens, in which the light that is generated with an energetic electron beam is collected and analyzed with a CL detector. CL provides a unique imaging contrast that can be used for visualizing growth zonation, distinguishing cement and granular detrital material, detecting trace elements, and characterizing fractures and deformation features in a large range of rocks, to name a few examples. In its simplest form CL imaging is performed with a static electron beam in an optical microscopy system (optical CL) but for more advanced experiments CL imaging is performed in a scanning electron microscope (SEM). This enables high scan speeds, high spatial resolution (< 100 nm), and correlation with other SEM based techniques such as X-ray imaging (EDS), secondary electron (SE) and backscattered electron (BSE) imaging, and more.</p><p>Currently, SEM-based CL work is mostly performed on costly floor model SEMs that require large amounts of space, complex auxiliary support systems, and an experienced operator to run the machine. In contrast, compact, affordable, and user-friendly table-top SEMs have improved substantially in the last years but they typically lack (advanced) CL imaging capabilities. Here, we will present our progress in developing a table-top SEM based CL system that can be used for geological research amongst other applications.</p><p>In particular, we have integrated a CL collection and detection system in a Thermo Fisher Scientific/Phenom XL table-top microscope, which already is equipped with SE, BSE, and EDS imaging modalities. In this SEM, electron energies of 5 – 15 keV can be used which is appropriate for most CL imaging experiments. The CL is collected using a multimode fiber optic cable connected with a graded index lens to increase the numerical aperture of the collection. Subsequently, the light is send to a spectrometer where the CL emission spectrum can be measured for every excitation point on the sample; a technique known as hyperspectral CL  imaging. To synchronize electron beam scanning with data acquisition and for data analysis we have developed dedicated software control.</p><p>We assess the potential of table-top CL by imaging representative polished zircon and quartz samples for various beam and acquisition parameters. To benchmark the system performance we compare our experimental results with results obtained from a state-of-the-art floor model SEM (Thermo Fisher Scientific Quanta 650 SFEG) system equipped with a high-end Delmic SPARC CL system. In the future, these developments may lower the threshold for using CL imaging through cost reduction and workflow simplification, making it accessible to a larger range of users within the field of geology and beyond.</p>

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