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Journal of Healthcare Engineering

Lina Al-Qatawneh, Abdallah A A Abdallah, Salam S Z Zalloum
Six Sigma is used heavily in various industrial sectors, yet no noticeable applications are seen in healthcare logistics. This paper reveals the special case of healthcare logistics where cost reduction is not the only factor considered in project selection; performance and criticality of each item in the logistics system are of high importance as well. This paper provides a proposed framework to apply Six Sigma in the area of healthcare logistics. It also presents a case study implementing the proposed framework at a Jordanian hospital...
2019: Journal of Healthcare Engineering
Ayush Goyal, Sunayana Tirumalasetty, Gahangir Hossain, Rajab Challoo, Manish Arya, Rajeev Agrawal, Deepak Agrawal
This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c -means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis...
2019: Journal of Healthcare Engineering
Subrahmanyam Murala, Santosh Kumar Vipparthi, Zahid Akhtar
No abstract text is available yet for this article.
2019: Journal of Healthcare Engineering
Lin Hou, Yinqiu Liu, Lixia Qian, Yucong Zheng, Jinnan Gao, Wenxing Cao, Yu Shang
Tissue hemodynamics, including the blood flow, oxygenation, and oxygen metabolism, are closely associated with many diseases. As one of the portable optical technologies to explore human physiology and assist in healthcare, near-infrared diffuse optical spectroscopy (NIRS) for tissue oxygenation measurement has been developed for four decades. In recent years, a dynamic NIRS technology, namely, diffuse correlation spectroscopy (DCS), has been emerging as a portable tool for tissue blood flow measurement. In this article, we briefly describe the basic principle and algorithms for static NIRS and dynamic NIRS (i...
2019: Journal of Healthcare Engineering
Jianjun He, Weirong Dai, Ying Li, Li He, Ruixue Huang
Background: Few studies have evaluated depression in female caregivers of patients with silicosis. Thus, the aim of this study was to estimate the prevalence of depression in such caregivers and to clarify the factors associated with symptoms of depression. Methods: Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D). Results: A total of 561 participants met the inclusion criteria and were enrolled in the study...
2019: Journal of Healthcare Engineering
Tae-Heon Yang, Jaeuk U Kim, Young-Min Kim, Jeong-Hoi Koo, Sam-Yong Woo
To meet the need for "standard" testing system for wearable blood pressure sensors, this study intends to develop a new radial pulsation simulator that can generate age-dependent reference radial artery pressure waveforms reflecting the physiological characteristics of human cardiovascular system. To closely duplicate a human cardiovascular system, the proposed simulator consists of a left ventricle simulation module, an aorta simulation module, a peripheral resistance simulation module, and a positive/negative pressure control reservoir module...
2019: Journal of Healthcare Engineering
Jingyi Yang, Quan Wei, Yanlei Ge, Lijiao Meng, Meidan Zhao
Objective: To assess the additional effect of self-management on physiotherapy via the use of APPS on management of chronic low back pain. Method: A single-blinded randomized control trial was conducted. 8 participants (male: 4; female: 4) were recruited from the Rehabilitation Clinic of The Hong Kong Polytechnic University. Participants in the treatment group received self-management plus physiotherapy and the control group received physiotherapy only. Assessment was carried out pretreatment, midterm (week 2), and posttreatment (week 4), including Visual Analog Scale (VAS), Pain Self-Efficacy Questionnaire (PSEQ), Roland Morris Disability Questionnaire (RMDQ), and SF36...
2019: Journal of Healthcare Engineering
Gulzar Ahmad, Muhammad Adnan Khan, Sagheer Abbas, Atifa Athar, Bilal Shoaib Khan, Muhammad Shoukat Aslam
In this research, a new multilayered mamdani fuzzy inference system (Ml-MFIS) is proposed to diagnose hepatitis B. The proposed automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system (ADHB-ML-MFIS) expert system can classify the different stages of hepatitis B such as no hepatitis, acute HBV, or chronic HBV. The expert system has two input variables at layer I and seven input variables at layer II. At layer I, input variables are ALT and AST that detect the output condition of the liver to be normal or to have hepatitis or infection and/or other problems...
2019: Journal of Healthcare Engineering
Ronny Brünler, Robert Hausmann, Maximilian von Münchow, Dilbar Aibibu, Chokri Cherif
An innovative approach for designing complex structures from STL-datasets based on novel software for assigning volumetric data to surface models is reported. The software allows realizing unique complex structures using additive manufacturing technologies. Geometric data as obtained from imaging methods, computer-aided design, or reverse engineering that exist only in the form of surface data are converted into volumetric elements (voxels). Arbitrary machine data can be assigned to each voxel and thereby enable implementing different materials, material morphologies, colors, porosities, etc...
2019: Journal of Healthcare Engineering
Giang Son Tran, Thi Phuong Nghiem, Van Thi Nguyen, Chi Mai Luong, Jean-Christophe Burie
Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans. Our method uses a novel 15-layer 2D deep convolutional neural network architecture for automatic feature extraction and classification of pulmonary candidates as nodule or nonnodule. Focal loss function is then applied to the training process to boost classification accuracy of the model...
2019: Journal of Healthcare Engineering
Ricardo Duarte, Jean-Pierre Nadeau, Antonio Ramos, Michel Mesnard
The orthosis is considered a class 1 medical device which often originates from a nonstructured development process. As these devices are mainly developed by small- and medium-sized enterprises, with no standard research method, the result can be an unadapted device which may not respond to the user's needs and which in the short term may be abandoned. One way to solve this problem is to define and apply standard rules and procedures throughout the development/design process. Although methodologies may solve the "empiricism" in orthosis design problems, these design strategies are not applied during orthosis development due to the particularities of this field and the difficulties in linking the required knowledge and the actors that may be present during the orthosis development...
2019: Journal of Healthcare Engineering
Ryo Sakamoto, Christopher Marano, Michael I Miller, Constantine G Lyketsos, Yue Li, Susumu Mori, Kenichi Oishi, Alzheimer's Disease Neuroimaging Initiative Adni
For patients with cognitive disorders and dementia, accurate prognosis of cognitive worsening is critical to their ability to prepare for the future, in collaboration with health-care providers. Despite multiple efforts to apply computational brain magnetic resonance image (MRI) analysis in predicting cognitive worsening, with several successes, brain MRI is not routinely quantified in clinical settings to guide prognosis and clinical decision-making. To encourage the clinical use of a cutting-edge image segmentation method, we developed a prediction model as part of an established web-based cloud platform, MRICloud...
2019: Journal of Healthcare Engineering
Liyun Bai, Ping Ji, Xian Li, Hui Gao, Linlin Li, Chao Wang
Individualized titanium mesh holds many advantages over conventional mesh. There are few reports in the literature about the effect of mesh pore size and mesh thickness on the mechanical properties of titanium mesh. This study is designed to develop an individualized titanium mesh using computer-assisted design and additive manufacturing technology. This study will also explore the effect of different thicknesses and pore sizes of titanium mesh on its mechanical properties through 3D FEA. According to this study, the mechanical properties of titanium mesh increased when the thickness decreased (0...
2019: Journal of Healthcare Engineering
Zhenglun Kong, Ting Li, Junyi Luo, Shengpu Xu
Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) on 5 MRI head image datasets...
2019: Journal of Healthcare Engineering
Mumtaz Hussain Soomro, Matteo Coppotelli, Silvia Conforto, Maurizio Schmid, Gaetano Giunta, Lorenzo Del Secco, Emanuele Neri, Damiano Caruso, Marco Rengo, Andrea Laghi
The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. The proposed CNN architecture, based on densely connected neural network, contains multiscale dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network...
2019: Journal of Healthcare Engineering
Li Luo, Xueru Xu, Yan Jiang, Wei Zhu
The vast majority of patients with intracerebral hemorrhage (ICH) suffer from long and uncertain length of stay (LOS). The aim of our study was to provide decision support for discharge and admission plans by predicting ICH patients' LOS probability distribution. The demographics, clinical predictors, admission diagnosis, and surgery information from 3,600 ICH patients were used in this study. We used univariable Cox analysis, multivariable Cox analysis, Cox-variable of importance (Cox-VIMP) analysis, and an intersection analysis to select predictors and used random survival forests (RSF)-a method in survival analysis-to predict LOS probability distribution...
2019: Journal of Healthcare Engineering
Wenjun Tan, Yue Yuan, Anning Chen, Lin Mao, Yuqian Ke, Xinhui Lv
Pulmonary vascular extraction from chest CT images plays an important role in the diagnosis of lung disease. To improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular extraction approach is proposed in this study. First, the lung tissue is extracted from chest CT images by region-growing and maximum between-class variance methods. Then the holes of the extracted region are filled by morphological operations to obtain complete lung region. Second, the points of the pulmonary vascular of the middle slice of the chest CT images are extracted as the original seed points...
2019: Journal of Healthcare Engineering
Christian Maier, Jan Christoph, Danilo Schmidt, Thomas Ganslandt, H U Prokosch, Stefan Kraus, Martin Sedlmayr
The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2...
2019: Journal of Healthcare Engineering
Taojin Xu, Zhongwei Jiang, Jongyeob Jeong, Minoru Morita, Hongbin Xu
To improve or maintain the physical function of bedridden patients, appropriate and effective exercises are required during the patient's bed rest. Resistance training (RT) is an effective exercise for improving the physical function of bedridden patients, and the improvement of the physical function is caused by mechanical stimuli associated with RT. Currently, the measured mechanical stimuli are external variables which represent the synthetic effect of multiple muscles and body movements. Important features of stimuli experienced by muscles are of crucial importance in explaining muscular strength and power adaptation...
2019: Journal of Healthcare Engineering
Alberto Ferrari, Luca Bergamini, Giorgio Guerzoni, Simone Calderara, Nicola Bicocchi, Giorgio Vitetta, Corrado Borghi, Rita Neviani, Adriano Ferrari
Diplegia is a specific subcategory of the wide spectrum of motion disorders gathered under the name of cerebral palsy. Recent works proposed to use gait analysis for diplegia classification paving the way for automated analysis. A clinically established gait-based classification system divides diplegic patients into 4 main forms, each one associated with a peculiar walking pattern. In this work, we apply two different deep learning techniques, namely, multilayer perceptron and recurrent neural networks , to automatically classify children into the 4 clinical forms...
2019: Journal of Healthcare Engineering
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