Fuzzy nature of decision-making process in healthcare enforces technology producers and researchers to employ creative and smooth solutions. Conversion from fuzzy concepts and ideas to crisp values causes loss of precision and weakens the output decisions. A promising bundle of techniques, soft computing, is a fast developing and popular area that helps meet this creative and smooth need in healthcare. In this study, fuzzy logic (FL) application in healthcare decision-making is examined. The number of publications is rising each year related to FL application in healthcare. FL can be used as a classifier, or in a selection process of a certain type of disease, or diseased patients, or determining the risk ratio of a disease, or in a data mining algorithm, or in constructing a decision support system. This study is a descriptive study aiming to examine and explain FL applications in healthcare.

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The electronic medical record (EMR) is now nearly ubiquitous in the USA. This article will review the EMR system with respect to goals, utilization, advantages compared with hand written records, as well as problems and/or disadvantages of the EMR system.

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Background and Objectives: Cloud-based mHealth services have the potential to make quality healthcare available in remote locations in the world. A practical deployment will involve medicolegal issues involving physicians and patients in different within and across countries. However, the first step is to evaluate such a cloud-based mHealth (MyOnlineClinic). This study aimed to understand and find out the factors that influence the end-user intention to use this new technology in Australia. Materials and Methods: We surveyed 167 end-users in 2015 and performed a Structural Equation Model analysis using Smart PLS to identify the intention to use the system among the participants. Results: The study revealed that the Unified Theory of Acceptance and Use of Technology construct, particularly facilitating condition (FC) (β = 0.355, P = 0.002), has yielded a significant influence on the behavioral intention to use MyOnlineClinic. However, the relationships between performance expectancy and behavioral intention (β = 0.162, P = 0.141), effort expectancy and behavioral intention (β = −0.004, P = 0.971), and social influences and behavioral intention (β = 0.164, P = 0.100) were insignificant. Further, age showed moderating effect on these variables. The majority of the respondents agreed or strongly agreed that technological issues such as sound (92.2%), video qualities (88.6%), and interaction with doctor (89.8%) are good. Conclusion: The end-users’ intentions to use MyOnlineClinic system were particularly influenced by FCs such as hardware, software, and the information technology knowledge/familiarity of users. These factors may get further accentuated when these systems are deployed across countries with different languages, technological infrastructures, and medicolegal environments. Therefore, cloud-based mHealth would help in removing some barriers, such as differences in software versions and interoperability problems of systems at physician and patient ends.

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Background and Objectives: Three-dimensional (3D) printing has been increasingly used in medicine with applications in the diagnostic assessment of disease extent, medical education and training, preoperative planning, and surgical simulation. The use of 3D printing in brain tumors is very limited. In this study, we presented our preliminary experience of creating patient-specific 3D printed model of a brain tumor in a pediatric patient and demonstrated the feasibility of using 3D printing in delineating brain anatomy and tumor. Materials and Methods: A life-size 3D printed brain model of a 6-year-old girl, who was diagnosed with pilocytic astrocytoma, was generated. The model was created using high-resolution magnetic resonance images which were postprocessed and segmented to demonstrate normal anatomical structures and the tumor. The tumor was confirmed to be Grade I pilocytic astrocytoma after neurosurgery. Results: 3D printed model was found to provide realistic visualization of brain anatomical structures and tumor, and enhance understanding of pathology in relation to the surrounding structures. The mean difference in diameter measurements of the brain tumor was 0.53 mm (0.98%) between the 3D printed model and computerized model. Conclusions: This study shows it is feasible to generate a 3D printed model of brain tumor with encouraging results achieved to replicate brain anatomy and tumor. 3D printed model of brain tumor could serve as an excellent tool for preoperative planning and simulation of surgical procedures, which deserve to be investigated in further studies.

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Background and Objectives: Spinal cord injury (SCI) is a severe disease where the patients lost the body function below the level of lesion. Neurorehabilitative exercise leads to improvements in physical functions such as strength, range of motion, transfers, wheelchair mobility, and gait. The aim of this study is to evaluate the impact of overground gait training using an active powered exoskeleton. Materials and Methods: Patients affected of SCI admitted to our rehabilitation unit have been studied. We used an active powered exoskeleton (Ekso). Training occurred each day for 5 days a week for a total of 4 weeks. Patients were trained for at least twenty sessions, with a duration of 45–60 min each session. Patients were scored with the 6 min walking test (6MWT) before and after treatment to evaluate the movement and Ashwort scale was used to test spasticity. Psychological tests were also performed to focus on depression (Beck Depression Inventory) and on self-perception (Body Uneasiness Test-A). Results: Thirteen patients were studied (mean age 31 ± 10.4; ten males and three females), who were affected by SCI with motor complete/incomplete lesions (seven complete, six incomplete), according to the American Spinal Injury Association guidelines. All patients completed the overground gait training for all 4 weeks without collateral effects. The motor recovery evaluated with the 6MWT in incomplete motor patients described a statistical significant recovery in terms of meters and absence of rest, especially in thoracic and lumbar level lesions (48/114 m [improvement 137.5%]; 98/214 m [improvement 118.37%], P < 0.05). We did not find any difference in terms of spasticity using the Ashworth Scale. After the treatment, we found in all patients a great improvement in mood disorders and body perception. Conclusions: The overground training with the exoskeleton is a promising therapeutical approach for SCI patients, which can increase both motor and psychological aspects.

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Advancements in technology have always had major impacts on medicine. Increased use of smartphone and related software applications has created a new era in clinical data exchange among patients and clinicians. Teledentistry is a combination of telecommunications and dentistry, involving the exchange of clinical information and images over remote distances for dental consultation and treatment planning. It has the potential to address many of the problems related to access, cost efficiency and quality of dental care. Through teleconsultation with specialists in larger communities, a dentist in a nearby community can provide access to specialty care for their patients easily. Teledentistry can extend care to underserved patient populations, such as those in rural areas, at a reasonable cost. This review article aims to emphasize the importance of teledentistry in various specialties of dentistry and its role in serving the underserved population.

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Background: In this article, a medical robotic system that performs magnetic resonance imaging-guided focused ultrasound surgery ablation is presented. The main innovation of this robotic system is that all the actuators are placed outside the water container. The transducer is immersed in water through an arm which is attached to the angular stage. Materials and Methods: The system includes three linear and one angular stage. The device uses piezoelectric motors for each motion stage. The accuracy was achieved with optical encoders. A focused transducer operated at 1 MHz with a radius of curvature of 10 cm and a diameter of 4 cm was used. A polyacrylamide gel was used to assess the ultrasound protocol. Results: The system was tested in the magnetic resonance imaging (MRI) environment and was proved to be a magnetic resonance compatible. The accuracy of the system was tested, and it was found that spatial steps of 0.2 mm can be safely and reliably achieved. With this robotic system, it is possible to access many organs that ultrasound penetrates with the patient placed in a prone position. Conclusion: The proposed robotic system can be modified so that it can be used for other applications. One example of an alternative application is MRI-guided biopsy. Another application is to replace the transducer arm with a radio frequency (RF) device to perform MRI guided RF ablation. Finally, the maneuverability of the robotic system can be enhanced further by attaching another angular stage to the system.

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Objective: Smartphones have evolved from luxury to essential need items in general population nowadays. iOS by Apple, Android by Google, Windows by Microsoft and Blackberry account for the majority of smartphone operating system which are currently in use today. Materials and Methods: The present study was done among 350 students of medical, dental, nursing and physiotherapy streams to know the prevalence of smartphone use and attitude towards its use. Of the 350 students enrolled in the study, 257 completed the questionnaire with a response rate of 73.4%. Results: Out of 222, 198 had android devices, 13 had iPhones, and 11 had other operating systems including Windows and Blackberry. 191 (74.3%) of the subjects had a working email account. Conclusion: First year students have positive perception towards smartphone use and medical schools should encourage the use of medical applications among them. However, students and medical professionals should be cautious of the negative issues with smartphone use.

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The pursuit of digital biomarkers wherein signal outputs from biosensors are collated to inform health-care decisions continues to evolve at a rapid pace. In the field of neurodegenerative disorders, a goal is to augment subjective patient-reported inputs with patient-independent verifiable data that can be used to recommend interventive measures. For example, in the case of Alzheimer's disease, such tools might preselect patients in the presymptomatic and prodromal phases for definitive positron emission tomographic analysis, allowing accurate staging of disease and providing a reference point for subsequent therapeutic and other measures. Selection of appropriate and meaningful digital biomarkers to pursue, however, requires deep understanding of the disease state and its ecological relationship to the instrumental activities of daily living scale. Similar opportunities and challenges exist in a number of other chronic disease states including Parkinson's, Huntington's, and Duchenne's disease, multiple sclerosis, and cardiovascular disease. This review will highlight progress in device technology, the need for holistic approaches for data inputs, and regulatory pathways for adoption. The review focuses on published work from the period 2012–2017 derived from online searches of the most widely used abstracting portals.

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Schwannoma, also called neurilemmoma, is a benign peripheral nerve sheath tumor. Most common locations are flexor surfaces of the extremities, neck, mediastinum, retroperitoneum, posterior spinal roots, and cerebellopontine angle. Pleural schwannomas are extremely rare neoplasms of the thoracic cavity. To the best of our knowledge, till date, <20 cases have been reported. Herein, we report a case of primary benign pleural schwannoma in an elderly 60-year-old female in whom the pleural mass was discovered incidentally on computed tomography chest. The patient was subjected to left posterolateral thoracotomy; pleural mass was resected which showed histopathological and immunohistochemical features of schwannoma.

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Background and Objectives: This study examined the learning effect of surface models made from the sectioned images of a male cadaver. Materials and Methods: The first author guided 46 medical students to study with a portable document format (PDF) file containing hundreds of surface models of a whole body. Their level of anatomy knowledge gained was evaluated by a digital examination on the tablet computers using the captured movies of PDF file. Results: The medical students' grades on the surface models were positively correlated with those on the remainder of the anatomy quiz. More than half of the students reported that the PDF file was helpful in anatomy learning and cadaver dissection. Conclusions: Digital anatomy learning is under continuous development in many ways. This report shows that surface models of the human body can be effective for self-learning and in the evaluation of anatomy knowledge.

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Wearable technologies designed to deliver benefits to health and well-being through the use of digital applications are becoming increasingly ubiquitous. This article focuses on the use of wearable technologies which track user lifestyle behaviors and seek to provide tools for better personal health management. It provides an evidence of general positive health outcomes from previous research and provides a detailed analysis of the functionalities and strategic approaches of three different wearable devices which have been used continuously and simultaneously by the lead author for over 18 months. Based on the experience of long-term use of these devices, the article draws some conclusions about their usage and future development strategies.

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Automatic processing large amount of microscopic images is important for medical and biological studies. Deep learning has demonstrated better performance than traditional machine learning methods for processing massive quantities of images; therefore, it has attracted increasing attention from the research and industry fields. This paper summarizes the latest progress of deep learning methods in biological microscopic image processing, including image classification, object detection, and image segmentation. Compared to the traditional machine learning methods, deep neural networks achieved better accuracy without tedious feature selection procedure. Obstacles of the biological image analysis with deep learning methods include limited training set and imperfect image quality. Viable solutions to these obstacles are discussed at the end of the paper. With this survey, we hope to provide a reference for the researchers conducting biological microscopic image processing.

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With the rapid development of the internet and information technologies, coupled with a variety of digital media, the digital technology has become a conventional method of health education for the general public and has the potential to influence health behaviors. Our aim was to conduct a review of how digital technology projects have been used in the health education and health promotion, as well as the disadvantages and barriers in the process.

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Digital imaging and communication in medicine (DICOM) is a standard that specifies a nonproprietary data exchange protocol, which was developed by the American College of Radiology and the National Electrical Manufacturers Association. It has now become the uncontested standard for the exchange and management of biomedical images. Here, we hope to highlight the significance of DICOM in digital imaging and to illustrate its indispensable role in digital imaging in the future.

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Objective: To assess prevalence, usage pattern, and risk of internet addiction (IA) among undergraduate students of a health institution from Delhi. Materials and Methods: A cross-sectional descriptive study was carried out during March-April 2015 using 20-item Young's IA test, a Likert scale-based interview schedule with scores ranging from 0 to 100 points with a higher score indicating greater internet dependency. Background variables included sociodemographic details, general health practices, self-assessment of mental health status, inter-personal relation (family/friends), personality type, and global satisfaction in life. The scoring pattern was analyzed in the form of low risk (score ≤49 points) and high risk (score ≥50 points) for IA. The proportion, Chi-square test, adjusted, and un-adjusted odds ratio (OR) (95% confidence interval) were computed using regression analysis. Results: Out of 202, 40.6% were MBBS students, followed by 35.6% from nursing, and 23.8% from medical lab technology stream; 68.3% were females; the mean age was 20.3 ± 1.4 years; and 61.9% were residing in hostels. It was observed that 44 (21.8%) and 22 (10.9%) students had ever consumed alcohol and smoked, respectively, while only 42 (20.8%) were engaged in physical activity (≥30 min) during most (≥5) of the days of the week. Based on self-assessment, 33 (16.3%) were globally dissatisfied and 88 (43.6%) reported themselves to be introverts. The majority of students were using internet for educational purpose (98%), entertainment (95.0%), accessing social sites (92.5%), checking E-mails (76.2%), and pornographic websites (45%). With regard to IA, 171 (84.7%) were at low risk (score ≤49) and 31 (15.4%) were at high risk (score ≥50). Male students (P = 0.001), ever consumed alcohol (P = 0.003), ever smoker (P = 0.02), and regular physical activity (P = 0.04) were found to be significantly associated with a high risk of IA based on Chi-square test, but none were found significant at higher levels of analyses (adjusted OR). No significant association of IA was found with mental status, global satisfaction, inter-personal relationship, or personality type. Conclusion: A large majority (84.7%) of students in our study are found to be at low risk of internet addiction.

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Background and Objectives: Stroke is a leading cause of long-term disability. Rehabilitation involving repetitive, high-intensity, and task-specific exercise is the pathway to restore motor skills. Robotic assistive devices such as Gloreha are increasingly being used in upper limb rehabilitation. The aim of this study is to explore the efficacy of robotic therapy for upper limb rehabilitation using robotic glove (Gloreha) in patients with stroke. Materials and Methods: The patients affected by stroke who were admitted to our rehabilitation unit were studied. Patients were exposed to Gloreha device rehabilitation (30 min/die), physiotherapy (1,5 hours/die), and occupational therapy (30 min/die). We measured the impairment in motor function and muscle tone using the modified Ashworth scale (MAS), the activities of daily living functional independence measure (FIM), and the finger dexterity Nine-Hole Peg test (NHPT). Results: Twelve patients (mean age = 64.5 years; male/female: 8:4) were admitted at the rehabilitation training. We found statistically significant differences between admission and discharged in terms of functional recovery using the FIM scale (pre/M = 88.33; post/M = 117.25,P = 0.01); hand training showed a better outcome using the NHPT (pre/M = 51.8; post/M = 36.33, P = 0.01). No significant changes were observed in terms of spasticity with the MAS (pre/M = 1.25; post/M = 1.08;P > 0.05). Conclusions: Rehabilitation with robotic glove (Gloreha) can positively promote functional recovery of arm function in a patient with stroke.

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Background and Objectives: Precision medicine and drug repurposing provide an opportunity to ameliorate the challenges of declining pharmaceutical R&D productivity, rising costs of new drugs, and poor patient response rates to existing medications. Multifactorial “disease signatures” provide unique insights into the architecture of complex disease populations that can be used to better stratify patient groups, aiding the delivery of precision medicine. Methods: Analysis of a complex disease (breast cancer) population was undertaken to identify the combinations of single-nucleotide polymorphisms that are associated with different disease subgroups. Target genes associated with the disease risk of these subgroups were examined, followed by identification and evaluation of existing active chemical leads as drug repurposing candidates. Results: One hundred and seventy-five disease-associated gene targets relevant to different subpopulations of breast cancer patients were identified. Twenty-three of these genes were prioritized as both promising novel drug targets and repurposing candidates. Two targets, P4HA2 and TGM2, have high repurposing potential and a strong mechanistic link to breast cancer. Conclusions: This study showed that detailed analysis of combinatorial genomic (and other) features can be used to accurately stratify patient populations and identify highly plausible drug repurposing candidates systematically across all disease-associated targets.

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This article reviews the role of mimicking materials used in focused ultrasound surgery (FUS) under magnetic resonance imaging. FUS is a noninvasive surgery that has many applications in oncology and neurology. Phantoms (mimicking materials) are mostly based in agar or gelatin phantoms.

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For decision support, a globally connected digital information system is desirable, which uses diagnostic findings and makes language independently statistical (anonymized) information from similar cases of all countries available. It can be realized efficiently in the following way: The definitions of all used diagnostics and measurement procedures are placed online. The defined data are called “Domain Vectors.” Doctors who use the online definitions get measurement results as Domain Vectors in comparable and searchable form. Anonymized selective statistics over patient groups with similar data can help to find the best treatment. Precondition for such distributed and simultaneously connected Domain Vectors are their global online definitions. Every Domain Vector only consists of a link to its definition (e.g., via URL or an abbreviated equivalent) plus numbers. This article explains details and concludes that introduction of the Domain Vectors with their online definitions would be an important step toward internationally connected medicine.

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Background and Objectives: Realistically and efficiently simulating dynamic behavior of human organs under interactions is crucial for the immersive user experience of the surgical simulator. Conventional methods are time-consuming to simulate this phenomenon due to topological modifications. Materials and Methods: This paper proposes a robust and efficient point-based framework for surgical simulation, allowing realistically simulating mechanical response of human organs under interactions with visual and tactile feedback. Considering the inevitable topological modifications occurred in surgical simulation, we adopt sparse point cloud to model the mechanics of deformable bodies while employ surface mesh to represent morphological details of human organ, which can not only disconnect mechanical complexity from geometrical details, but also enable precise boundary conditions to be solved with surface mesh. Results: We validate our method on a variety of challenging surgical scenarios, and the results demonstrate that our method can realistically and efficiently provide the visuo-haptic feedback for surgical simulation. Conclusions: Our method can well tackle the inefficiency limitation of mesh-based methods related to topological modifications issue, and has great potential to be adopted in practical surgical simulators.

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Background and Objectives: Patient safety in hospital is an essential professional indicator that should be noticed. The thread of fires is of the most potentially dangerous risk that could harm patients and personnel. Hence, we studied operating room staff knowledge about fires, its prevention, and control, based on their specialty and their job tenure. Materials and Methods: This is a descriptive-analytical cross-sectional study. A cluster sampling method was applied, and a quota was considered for each hospital. In each cluster, samples were selected through census method. Data were collected using a three-part questionnaire and analyzed using SPSS version 17 (SPSS, Inc, Chicago, IL, USA). Results: The mean of their knowledge level about fires and its prevention was 2.71 (0.67), and control was 2.62 (0.71). In 66% of cases, the level of knowledge about fires and its prevention, and in 70% of cases, the level of knowledge about methods of fire control have been below the average (P < 0.05). As staff age increases, their level of knowledge increases as well. Hospital they are employed by, their job tenure, and their profession can affect the level of knowledge (P < 0.05). Discussion: This study shows that the staff knowledge about fires, its prevention, and control has been lower than the average limit. Due to the sensitivity of the issue, in terms of susceptibility of the environment and medical equipment for ignition, and also the safety of staff and patients, it is necessary to make facilities for development of staff knowledge, to eliminate defects of staff training by managers, and also to consider standards.

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Cone beam computed tomography (CBCT) is a developing imaging technique designed to provide relatively low-dose high-spatial-resolution visualization of high-contrast structures in the head and neck and other anatomic areas. It is a vital content of a dental patient's record. A literature review demonstrated that CBCT has been utilized for oral diagnosis, oral and maxillofacial surgery, endodontics, implantology, orthodontics; temporomandibular joint dysfunction, periodontics, and restorative and forensic dentistry. Recently, higher emphasis has been placed on the CBCT expertise, the three-dimensional (3D) images, and virtual models. This literature review showed that the different indications for CBCT are governed by the needs of the specific dental discipline and the type of procedure performed.

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