scholarly journals Evaluation of the Accuracy of a System to Align Occlusal Dynamic Data on 3D Digital Casts

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Iñigo de Prado ◽  
Mikel Iturrate ◽  
Rikardo Minguez ◽  
Eneko Solaberrieta
Keyword(s):  
Human Error ◽  
High Reliability ◽  
Usual Method ◽  
Specific Method ◽  
Alignment Method ◽  
Common Procedure ◽  
Dynamic Data ◽  
Remarkable Advance ◽  
Paper Method

In recent years the T-Scan system has introduced the possibility of importing digitization of dental arches to its registrations. This is a remarkable advance, which allows an intuitive display of the location of the gathered dynamic data on the denture. Nevertheless, today’s usual method of manually positioning the arch in relation to the T-Scan’s force registration gives rise to the possibility of human error. In order to guarantee a good alignment between the dynamic registration and 3D digital casts, a specific method was developed. The aim of this study is to evaluate the accuracy of this alignment method. For this purpose, it was compared with the most common procedure for detecting occlusal contacts, the articulating paper method. The comparison comprised overlapping digital models of both methods. Contacts of casts of 11 adults were registered, both with articulating paper and the T-Scan system. For one method, articulating paper marks were scanned in color; for the second method, the previously mentioned alignment was carried out with the T-Scan registrations. The results of both methods were overlapped in 3D digital casts, quantifying occlusal data matches. Statistical analyses were made to measure the quality of this alignment method. The study revealed a mean matching percentage of 79.02%, confirming the high reliability of the method.

Pengaruh Kompetensi Coder terhadap Keakuratan dan Ketepatan Pengkodean Menggunakan ICD 10 di Rumah Sakit X Pekanbaru Tahun 2016

2018 ◽  
Vol 1 (1) ◽  
pp. 31-43
Author(s):  
Nur Maimun ◽  
Jihan Natassa ◽  
Wen Via Trisna ◽  
Yeye Supriatin
Keyword(s):  
Medical Record ◽  
Human Error ◽  
Medical Personnel ◽  
Quality Of Data ◽  
Accuracy And Precision ◽  
Result Show ◽  
Study Implementation ◽  
Icd 10 ◽  
The Cost

The accuracy in administering the diagnosis code was the important matter for medical recorder, quality of data was the most important thing for health information management of medical recorder. This study aims to know the coder competency for accuracy and precision of using ICD 10 at X Hospital in Pekanbaru. This study was a qualitative method with case study implementation from five informan. The result show that medical personnel (doctor) have never received a training about coding, doctors writing that hard and difficult to read, failure for making diagnoses code or procedures, doctor used an usual abbreviations that are not standard, theres still an officer who are not understand about the nomenclature and mastering anatomy phatology, facilities and infrastructure were supported for accuracy and precision of the existing code. The errors of coding always happen because there is a human error. The accuracy and precision in coding very influence against the cost of INA CBGs, medical and the committee did most of the work in the case of severity level III, while medical record had a role in monitoring or evaluation of coding implementation. If there are resumes that is not clearly case mix team check file needed medical record the result the diagnoses or coding for conformity. Keywords: coder competency, accuracy and precision of coding, ICD 10

Project Review on Water Level Sensing Using PLC

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Osama Mahfooz ◽  
Mujtaba Memon ◽  
Asim Iftikhar
Keyword(s):  
Automatic Control ◽  
Water Level ◽  
Digital Computer ◽  
Human Error ◽  
Water Tank ◽  
Industrial Plants ◽  
Commercial Use ◽  
The Cost ◽  
Water Tanks

<span>A PLC is a digital computer used to automate electromechanical processes. This research is<span> based on automation of a water tank by using Siemens PLC. Automatic control of water tanks<span> can work continuously and can provide accurate quantity of water in less time. In such process<span> there is no need of labor so there is no human error. Without human error, the quality of product<span> is better and the cost of production would definitely decrease with no error in quantity required.<span> Water level sensing can be implemented in industrial plants, commercial use and even at home<br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span>

scholarly journals Not all biases are bad: equitable and inequitable biases in machine learning and radiology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mirjam Pot ◽  
Nathalie Kieusseyan ◽  
Barbara Prainsack
Keyword(s):  
Machine Learning ◽  
Human Error ◽  
Technical Problem ◽  
Social Dimension ◽  
Patient Treatment ◽  
Technical Solution ◽  
Negative Effects ◽  
Different Types ◽  
Human Decision

AbstractThe application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the ‘distorted’ outcomes they yield often have implications for equity. This paper assesses different types of biases that can emerge within applications of ML in radiology, and discusses in what cases such biases are problematic. Drawing upon theories of equity in healthcare, we argue that while some biases are harmful and should be acted upon, others might be unproblematic and even desirable—exactly because they can contribute to overcome inequities.

scholarly journals Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4613
Author(s):  
Yi-Cheng Mao ◽  
Tsung-Yi Chen ◽  
He-Sheng Chou ◽  
Szu-Yin Lin ◽  
Sheng-Yu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Rapid Development ◽  
Radiographic Examination ◽  
Analysis Model ◽  
Common Procedure ◽  
Medical Treatments ◽  
Pass Filter ◽  
Single Tooth ◽  
Imaging Research

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

scholarly journals An Organizational Learning Framework for Patient Safety

2016 ◽  
Vol 32 (2) ◽  
pp. 148-155 ◽  
Author(s):  
Marc T. Edwards
Keyword(s):  
Patient Safety ◽  
Organizational Learning ◽  
Human Error ◽  
High Reliability ◽  
Organizational Capabilities ◽  
Organizational Learning Theory ◽  
Culture Of Safety ◽  
Learning Framework ◽  
Operational Improvement ◽  
Learning Modes

Despite concerted effort to improve quality and safety, high reliability remains a distant goal. Although this likely reflects the challenge of organizational change, persistent controversy over basic issues suggests that weaknesses in conceptual models may contribute. The essence of operational improvement is organizational learning. This article presents a framework for identifying leverage points for improvement based on organizational learning theory and applies it to an analysis of current practice and controversy. Organizations learn from others, from defects, from measurement, and from mindfulness. These learning modes correspond with contemporary themes of collaboration, no blame for human error, accountability for performance, and managing the unexpected. The collaborative model has dominated improvement efforts. Greater attention to the underdeveloped modes of organizational learning may foster more rapid progress in patient safety by increasing organizational capabilities, strengthening a culture of safety, and fixing more of the process problems that contribute to patient harm.

The Application of S7-200 PLC Reactive Compensation Device in Small Substation

2012 ◽  
Vol 479-481 ◽  
pp. 1476-1480
Author(s):  
Qing He Chu ◽  
Wen Si Cao ◽  
Zhen Nie
Keyword(s):  
Reactive Power ◽  
Control Method ◽  
High Reliability ◽  
Reactive Power Compensation ◽  
Control Device ◽  
Reactive Compensation ◽  
Reconstruction Project ◽  
Compensation Device ◽  
And Control

In the present rural power grid reconstruction project ,according to the problems of capacitor reactive power compensation in rural small substation. Take a small substation for instance, according to its operation and control method, set the criterion of the capacitor bank switching. A new high reliability, flexible reactive power compensation control device based on s7-200 PLC is designed. It plays an important role in improving the power supply , guaranteeing the quality of voltage, reducing the loss of rural power gid.

DYNAMICS UNDERLYING PATIENT-VENTILATOR INTERACTIONS DURING NOCTURNAL NONINVASIVE VENTILATION

2012 ◽  
Vol 22 (02) ◽  
pp. 1250030 ◽  
Author(s):  
R. NAECK ◽  
D. BOUNOIARE ◽  
U. S. FREITAS ◽  
H. RABARIMANANTSOA ◽  
A. PORTMANN ◽  
...  
Keyword(s):  
Time Series ◽  
Respiratory Failure ◽  
Shannon Entropy ◽  
Noninvasive Ventilation ◽  
Spontaneous Breathing ◽  
Sleep Stages ◽  
Common Procedure ◽  
Pressure Time ◽  
Ventilatory Assistance

Noninvasive ventilation is a common procedure for managing patients having chronic respiratory failure. The success of this ventilatory assistance is often linked with patient's tolerance that is known to be related to the quality of the synchronization between patient's spontaneous breathing cycles and ventilatory cycles delivered by the ventilator. Thirty-four sleep sessions (more than 5000 ventilatory cycles each) were automatically investigated using a specific algorithm processing airflow and pressure time series. Four groups of patients were defined according to the interplay between asynchrony events and leaks. Different mechanisms that depend on sleep stages were thus evidenced. A Shannon entropy was also proposed as a new sleep fragmentation quantification methodology.

scholarly journals Assessment of neuropsychiatric symptoms in dementia: Toward improving accuracy

2013 ◽  
Vol 7 (3) ◽  
pp. 244-251 ◽  
Author(s):  
Florindo Stella
Keyword(s):  
Rating Scale ◽  
High Reliability ◽  
Neuropsychiatric Symptoms ◽  
Clinical Approach ◽  
Behavioral Disturbances ◽  
Versatile Tool ◽  
Clinician Rating ◽  
Improving Accuracy ◽  
Elderly Caregivers

ABSTRACT The issue of this article concerned the discussion about tools frequently used tools for assessing neuropsychiatric symptoms of patients with dementia, particularly Alzheimer's disease. The aims were to discuss the main tools for evaluating behavioral disturbances, and particularly the accuracy of the Neuropsychiatric Inventory - Clinician Rating Scale (NPI-C). The clinical approach to and diagnosis of neuropsychiatric syndromes in dementia require suitable accuracy. Advances in the recognition and early accurate diagnosis of psychopathological symptoms help guide appropriate pharmacological and non-pharmacological interventions. In addition, recommended standardized and validated measurements contribute to both scientific research and clinical practice. Emotional distress, caregiver burden, and cognitive impairment often experienced by elderly caregivers, may affect the quality of caregiver reports. The clinician rating approach helps attenuate these misinterpretations. In this scenario, the NPI-C is a promising and versatile tool for assessing neuropsychiatric syndromes in dementia, offering good accuracy and high reliability, mainly based on the diagnostic impression of the clinician. This tool can provide both strategies: a comprehensive assessment of neuropsychiatric symptoms in dementia or the investigation of specific psychopathological syndromes such as agitation, depression, anxiety, apathy, sleep disorders, and aberrant motor disorders, among others.

scholarly journals Development of the TabacoQuest app for computerization of data collection on smoking in psychiatric nursing

2016 ◽  
Vol 24 (0) ◽  
Author(s):  
Renata Marques de Oliveira ◽  
Alexandre Freitas Duarte ◽  
Domingos Alves ◽  
Antonia Regina Ferreira Furegato
Keyword(s):  
Data Collection ◽  
Human Error ◽  
Technological Development ◽  
Psychiatric Patients ◽  
Mobile App ◽  
Psychiatric Nursing ◽  
Field Validation ◽  
Final Test ◽  
Computational Resources

ABSTRACT Objective: to develop a mobile app for research on the use of tobacco among psychiatric patients and the general population. Method: applied research with the technological development of an app for data collection on an Android tablet. For its development, we considered three criteria: data security, benefits for participants and optimization of the time of researchers. We performed tests with twenty fictitious participants and a final test with six pilots. Results: the app collects data, stores them in the database of the tablet and export then to an Excel spreadsheet. Resources: calculator, stopwatch, offline operation, branching logic, field validation and automatic tabulation. Conclusion: the app prevents human error, increases the quality of the data by validating them during the interview, allows the performing of automatic tabulation and makes the interviews less tiring. Its success may encourage the use of this and other computational resources by nurses as a research tool.

scholarly journals Cluster-Based Cooperative Cache Deployment and Coded Delivery Strategy in C-V2X Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Haizhou Bao ◽  
Yiming Huo ◽  
Chuanhe Huang ◽  
Xiaodai Dong ◽  
Wanyu Qiu
Keyword(s):  
High Reliability ◽  
Cluster Head ◽  
Cluster Method ◽  
Response Delay ◽  
Delivery Strategy ◽  
Network Load ◽  
Cooperative Cache ◽  
Dynamic Topology ◽  
Cache Hit Ratio

Cellular vehicle-to-everything- (C-V2X-) based communications can support various content-oriented applications and have gained significant progress in recent years. However, the limited backhaul bandwidth and dynamic topology make it difficult to obtain the multimedia service with high-reliability and low-latency communication in C-V2X networks, which may degrade the quality of experience (QoE). In this paper, we propose a novel cluster-based cooperative cache deployment and coded delivery strategy for C-V2X networks to improve the cache hit ratio and response time, reduce the request-response delay, and improve the bandwidth efficiency. To begin with, we design an effective vehicle cluster method. Based on the constructed cluster, we propose a two-level cooperative cache deployment approach to cache the frequently requested files on the edge nodes, LTE evolved NodeB (eNodeB) and cluster head (CH), to maximize the overall cache hit ratio. Furthermore, we propose an effective coded delivery strategy to minimize the network load and the ratio of redundant files. Simulation results demonstrate that our proposed method can effectively reduce the average response delay and network load and improve both the hit ratio and the ratio of redundant files.

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