scholarly journals Influence of different types of real-time feedback on hand washing quality assessed with neural networks/simulated neural networks

2022 ◽  
Vol 131 ◽  
pp. 02008
Author(s):  
Olga Zemlanuhina ◽  
Martins Lulla ◽  
Aleksejs Rutkovskis ◽  
Andreta Slavinska ◽  
Aija Vilde ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hand Hygiene ◽  
Real Time ◽  
Type A ◽  
Hand Washing ◽  
Clinical Environment ◽  
Hand Wash ◽  
Study Session ◽  
Type C ◽  
Overall Performance

Background: Thousands of people die every day around the world from infections acquired in a hospital. Hands are the main pathways of germ transmission during healthcare. Hand hygiene monitoring can be performed using various methods. One of the latest techniques that can combine all is a neural network-based hand hygiene monitoring system. Methods/Design: Each participant performed 3 hand-washing trials, each time receiving different type of feedback. The order in which each participant of the study used the developed applications was strictly defined, thus each hand-washing study session started with performing hand washing using application A, B and C accordingly. All captured videos of hand-wash episodes were saved and later analysed with neural networks. In the end, both evaluation results were compared and evaluated. Results show that when the participants use Application Type A, they perform hand washing much faster, as well as in comparison of Application Type A versus application type C. However, the longest time spent for the hand washing was detected while using the application type B. Conclusion: Study shows that structured guidance provided during the real time hand washing could be associated with better overall performance. The Application C has confirmed its effectiveness. Proving its advantage among other applications, the Application C can be integrated into the clinical environment

scholarly journals Application of Real-Time Tissue Elastography with a Low Frequency Convex Array Probe: A Noninvasive Approach to Differential Diagnosis of Liver Tumors

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Juan Wang ◽  
Hong Ai ◽  
Long Guo ◽  
Lifang Tan ◽  
Huilin Gong ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Real Time ◽  
Liver Tumors ◽  
Low Frequency ◽  
Type A ◽  
Benign Tumors ◽  
Array Probe ◽  
Type C ◽  
Sensitivity Specificity ◽  
Tissue Elastography

To evaluate diagnostic performance of real-time tissue elastography (RTE) with a low frequency convex array probe for distinguishing benign from malignant hepatic tumors through trans-abdominal examination, elasticity images of 210 liver tumors were obtained by EUB-7500 (Hitachi Medical Systems and 3.5 MHz probe) and eventually 121 liver tumors were analyzed in the study. Elasticity images were classified into four types, from type a to d. Regarding type a or b as benign tumors and type c or d as malignant ones, sensitivity, specificity, and accuracy were calculated and the consistency between the findings of RTE and the pathohistological diagnosis was evaluated. The sensitivity, specificity, and accuracy were separately 97.2%, 88.0%, and 93.4% (P<0.001). Moreover, there was a good consistency between the findings of RTE and the pathological diagnosis (kappa value 0.86). Among elasticity images of all the malignant tumors, the hepatocellular carcinomas (HCCs) mainly appeared in type c, and liver metastatic cancers in type d. Thus, RTE utilized as a novel noninvasive imaging examination method enables us to distinguish benign from malignant liver tumors. Moreover, it provides certain information for the differential diagnosis between HCCs and liver metastatic cancers.

Electronic Hand Hygiene Quality and Duration Monitoring in Pre-School Children: A Feasibility Study

2021 ◽  
pp. 0272684X2110334
Author(s):  
Mohd Dzulkhairi Mohd Rani ◽  
Nurul Azmawati Mohamed ◽  
Tengku Zetty Maztura Tengku Jamaluddin ◽  
Zarini Ismail ◽  
Shalinawati Ramli ◽  
...  
Keyword(s):  
Data Collection ◽  
Hand Hygiene ◽  
Electronic Device ◽  
Hand Washing ◽  
World Health ◽  
Who Guidelines ◽  
Hygiene Practices ◽  
Hand Wash ◽  
The World ◽  
Health Organization

Background Hand hygiene is regarded as the most important measure to prevent spread of infectious diseases. The aim of this study was to assess the feasibility of a prototype application in an electronic device in educating, stimulating and monitoring hand hygiene quality in young children. Method A pre-school was provided with an interactive hand hygiene application for two months. The device features an online administrator dashboard for data collection and for monitoring the children’s hand washing steps and duration. A good hand washing is defined as hand washing which comprise all of the steps outlined in the World Health Organization (WHO) guidelines. Results The prototype managed to capture 6882 hand wash performed with an average of 20.85 seconds per hand wash. Washing hands palm to palm was the most frequent (79.9%) step performed, whereas scrubbing fingernails and wrists were the least (56%) steps performed. Conclusions The device is a good prototype to educate, stimulate and monitor good hand hygiene practices. However, other measures should be undertaken to ensure sustainability of the practices.

scholarly journals Hubungan Supervisi dan Motivasi Dengan Kepatuhan Perawat Mencuci Tangan Di RSUD

2019 ◽  
Vol 2 (1) ◽  
pp. 28
Author(s):  
Danuarsa Parwa ◽  
Menik Sri Krisnawati ◽  
Emy Darma Yanti
Keyword(s):  
Hand Hygiene ◽  
Hand Hygiene Compliance ◽  
Hand Washing ◽  
P Value ◽  
Cross Sectional ◽  
Motivation Factors ◽  
Hand Wash ◽  
Cross Sectional Design ◽  
Hygiene Compliance ◽  
Health Care Associated Infection

Health-care Associated Infection (HAIs) is a serious health problem and impact the country's economic burden. The efforts of hand wash is to prevent HAIs. Head of Space supervision and nurse’s motivation factors are affect hand washing compliance that remain poor among nurses. This research was conducted to determine the relationship between Head of Space supervision and nurse’s motivation with hand washing compliance in RSUD Y in 2018. This research was descriptive correlation with cross sectional design. The samples were 33 associate nurse through total sampling. The data was collected by questionnare and observation sheet. This research showed there is relationship between Head of Space supervision with hand washing compliance (p-value = 0.014, r = 0.423) and there is relationship between nurse’s motivation with hand hygiene compliance (p- value = 0.012, r = 0.433). This research concluded were Head of Space supervision by doing bad will decreased hand hygiene compliance and if nurse’s motivation increases will increased hand hygiene compliance among nurses in X room RSUD

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

scholarly journals Transformer-based artificial neural networks for the conversion between chemical notations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lev Krasnov ◽  
Ivan Khokhlov ◽  
Maxim V. Fedorov ◽  
Sergey Sosnin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rapid Development ◽  
Performance Level ◽  
Rule Based ◽  
Development Costs ◽  
Overall Performance ◽  
Artificial Neural

AbstractWe developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.

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