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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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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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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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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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 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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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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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: 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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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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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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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: Three-dimensional (3D) printing has potential value in medical applications with increasing reports in the diagnostic assessment of cardiovascular diseases. The use of 3D printing in replicating pulmonary artery anatomy and diagnosing pulmonary embolism is very limited. The purpose of this study was to develop a 3D printed pulmonary artery model and test different computed tomography (CT) scanning protocols for determination of an optimal protocol with acceptable image quality but low radiation dose. Materials and Methods: A patient-specific 3D printed pulmonary artery model was created based on contrast-enhanced CT images in a patient with suspected pulmonary embolism. Different CT pulmonary angiography protocols consisting of 80, 100, and 120 kVp, pitch 0.7, 0.9, and 1.2 with 1 mm slice thickness, and 0.6 mm reconstruction interval were tested on the phantom. Quantitative assessment of image quality in terms of signal-to-noise ratio (SNR) was measured in the images acquired with different protocols. Measurements in pulmonary artery diameters were conducted and compared between pre- and post-3D printed images and 3D printed model. Results: The 3D printed model was found to replicate normal pulmonary artery with high accuracy. The mean difference in diameter measurements was <0.8 mm (<0.5% deviation in diameter). There was no significant difference in SNR measured between these CT protocols (P = 0.96–0.99). Radiation dose was reduced by 55% and 75% when lowering kVp from 120 to 100 and 80 kVp, without affecting image quality. Conclusions: It is feasible to produce a 3D printed pulmonary artery model with high accuracy in replicating normal anatomy. Different CT scanning protocols are successfully tested on the model with 80 kVp and pitch 0.9 being the optimal one with resultant diagnostic images but at much lower radiation dose.

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Background and Objectives: This paper evaluates the performance of a variational level set method for performing label fusion through the use of a penalty term, label fusion term, and length regularization term, which automatically labels objects of interest in biomedical images. This paper is an extension of our preliminary work in the conference paper. We mainly focus on the validation of the variational level set method. Subjects and Methods: Label fusion is achieved by combining the three terms: label fusion term, image data term, and regularization term. The curve evolution derived from the energy minimization is impacted by the three terms simultaneously to achieve optimal label fusion. Each label obtained from the nonlinear registration method is represented by a level set function whose zero level contour encloses the labeled region. In Lu et al.’s paper, they employ the level set formulation only for hippocampus segmentation. Results: Our method is compared with majority voting (MV), local weighted voting (LWV), and Simultaneous Truth and Performance Level Estimation (STAPLE). The method is evaluated on MICCAI 2012 Multi-Atlas Labeling challenge and MICCAI 2012 ventricle segmentation challenge. The mean Dice metric is computed using different atlases and produces results with 0.85 for the hippocampus, 0.77 for the amygdala, 0.87 for the caudate, 0.78 for the pallidum, 0.89 for the putamen, 0.91 for the thalamus, and 0.78 for cardiac left ventricles. Conclusions: Experimental results demonstrate that our method is robust to parameter setting and outperforms MV, LWV, and STAPLE. The image data term plays a key role in improving the segmentation accuracy. Our method can obtain satisfactory results with fewer atlases.

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Objective: To investigate the causes for retinal artery occlusion and cerebral infarction resulting from cosmetic facial autologous fat injections. Materials and Methods: Twenty-seven retrospective, noncomparative cases with artery occlusion caused by facial autologous fat injections were included. Injection sites, basic demographic information (age, sex, and laterality of the involved eyes), best-corrected visual acuities (BVCAs), fundus fluorescein angiography, optical coherence tomography findings, brain magnetic resonance imaging, and associated ocular and systemic manifestations were collected as study information. Twenty-seven cases were classified according to artery occlusion, which led to blindness and brain infarction, as relationship between blindness and brain infarction may provide clues to help us figure out the process of arterial blockage. Results: Of the 27 cases, 13 patients had ophthalmic artery occlusion (OAO), 6 had central retinal artery occlusion (CRAO), and 3 had branch retinal artery occlusion (BRAO). Injection sites were the glabellar area (9 cases), nasolabial area (5 cases), forehead area (4 cases), periocular area (2 cases), nose area and nasal area (2 cases), multiple places (2 cases), and other areas (3 cases). Injection at different injection sites may lead to blindness, which means that emboli went into the blood by different branches of the external carotid artery, and eventually blocked the ophthalmic artery and its branches. Concomitant brain infarction developed in 13 cases with retinal artery occlusion. The high probability of occurrence of cerebral infarction indicated that internal carotid artery could be a flow path of emboli. Conclusion: Cosmetic facial autologous fat injections may cause retinal artery occlusion. Under the pressure of injection, fat emboli go through the terminal artery of face into the ophthalmic artery counter currently. In some cases, retrograde arterial embolism also causes brain infarction. Middle cerebral artery occlusion is closely associated with OAO.

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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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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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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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[INLINE:1] Uwe Spetzger, MD, is the Chairman of Department of Neurosurgery, Klinikum Karlsruhe, and Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. Uwe Spetzger received his medical degree in 1989 at the Medical Faculty, University of Heidelberg, Germany. He passed the US American medical exam (ECFMG) in 1990. He has started his neurosurgical training in 1990 at the Department of Neurosurgery, Technical University (RWTH) Aachen and got his board certification of neurosurgeon in 1996. The Grant of the Wilhelm-Tonnis-Foundation of the German Society of Neurosurgery (DGNC) enables his scientific internship in 1999 at Department of Neurosurgery, University of Illinois at Chicago (UIC). In June 1999, he passed the European Examination in Neurosurgery (EANS). From 1999 - 2002 he was vice-chairman of the Department of Neurosurgery at the University of Freiburg and the director of the interdisciplinary centre for skull base surgery at Freiburg University. Since 2002 he is Chairman of the Department of Neurosurgery, Klinikum Karlsruhe and in 2003 he became an active member of the Faculty of Computer Science, Humanoids and Intelligence Systems Lab - Institute for Anthropomatics at KIT. Prof. Spetzger became the president of the international Society of Medical Innovation and Technology iSMIT in 2013 and the congress president of the 66th Annual Meeting of German Society of Neurosurgery DGNC in 2015 and the Vice-President of the International Society of Digital Medicine in 2016. He is member of several national and international neurosurgical and medical technological societies. His main surgical and research interests are cerebrovascular surgery, skull base surgery, computer-assisted and robotic surgery, neuronavigation and spinal microsurgery.

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Background and Objectives: Lung cancer is the commonest cancer killer in the western world. Many patients have lung cancers first identified on chest radiograph (CXR). Potentially curable cancers are often missed on CXR. This study quantified the incidence of cases of lung cancer which were initially overlooked and studied the causes of delayed diagnosis. Materials and Methods: All consecutive patients discussed during a 3-month period at the local lung cancer multidisciplinary meeting (MDM) were identified. All imaging within two years prior to diagnosis of lung cancer were reviewed with its report. Any CXR examination which failed to raise the potential for lung cancer was blindly reviewed by four consultant chest radiologists. Results: 189 patients were identified from the MDM over three months. 38,049 CXRs were carried out in the trust over the same period. Of the 189 patients, 58 had previous CXRs within 2 years reported as normal. On review 27 (47%) showed an abnormality in the region of the lung subsequently shown to have cancer. 70% of lesions were central, obscured by the heart, diaphragm, clavicles or mediastinum. Conclusions: This study shows that 1 in 1,409 CXRs reported as normal harbours a visible lung cancer on retrospective review. In this group 14% of patients with lung cancer could potentially have been diagnosed earlier. Of those that had previous CXRs, 47% had abnormal CXRs reported as normal. This study qualifies the rate of missed lung cancer on CXR in clinical practice and highlights where on CXR cancers are missed.

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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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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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