Color Doppler US can be used to accurately diagnose CTS. By processing the recorded power Doppler images and determining the number of pixels in the intraneural vascular area, the severity of CTS can be assessed. 2011 SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110150/-/DC1.

Cancer is the second leading cause of death after cardiovascular diseases in the world. Health professionals are seeking ways for suitable treatment and quality of care in these groups of patients. Survival prediction is important for both physicians and patients in order to choose the best way of management. Artificial Neural Network (ANN) is one of the most efficient data mining methods. This technique is able to evaluate the relationship between different variables spontaneously without any prevalent data. In our study ANN and Logistic Regression were used to predict survival in thyroid cancer and compare these results. SEER (Surveillance, Epidemiology and End Result) data were got from SEER site 1 . Effective features in thyroid cancer have been selected based on supervision by radiation oncologists and evidence. After data pruning 7706 samples were studied with 16 attributes. Multi Layer Prediction (MLP) was used as the chosen neural network and survival was predicted for 1-, 3-and 5-years. Accuracy, sensitivity and specificity were parameters to evaluate the model.

Background:The unexpected emergence of coronavirus disease 2019 (COVID-19) has changed mindsets about the healthcare system and medical practice in many fields, forcing physicians to reconsider their approaches to healthcare provision. It is necessary to add new, unique, and efficient solutions to traditional methods to overcome this critical challenge. In this regard, telemedicine offers a solution to this problem. Remote medical activities could diminish unnecessary visits and provide prompt medical services in a timely manner.ObjectiveThis scoping review aimed to provide a map of the existing evidence on the use of telemedicine during the COVID-19 pandemic by focusing on delineation functions and technologies, analyzing settings, and identifying related outcomes.MethodsThis review was conducted following the Arksey and O'Malley framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist. PubMed and Scopus databases were systematically searched based on specific eligibility criteria. The English publications included in this study focused on telemedicine systems implemented during the COVID-19 pandemic to provide clinical care services. Two independent reviewers screened the articles based on predefined inclusion and exclusion criteria. The relevant features of telemedicine systems were summarized and presented into the following four domains and their subcategories, including functionality, technology, context, and outcomes.ResultsOut of a total of 1,602 retrieved papers, 66 studies met the inclusion criteria. The most common function implemented was counseling, and telemedicine was used for diagnosis in seven studies. In addition, in 12 studies, tele-monitoring of patients was performed by phone, designed platforms, social media, Bluetooth, and video calls. Telemedicine systems were predominantly implemented synchronously (50 studies). Moreover, 10 studies used both synchronous and asynchronous technologies. Although most studies were performed in outpatient clinics or centers, three studies implemented a system for hospitalized patients, and four studies applied telemedicine for emergency care. Telemedicine was effective in improving 87.5% of health resource utilization outcomes, 85% of patient outcomes, and 100% of provider outcomes.ConclusionThe benefits of using telemedicine in medical care delivery systems in pandemic conditions have been well–documented, especially for outpatient care. It could potentially improve patient, provider, and healthcare outcomes. This review suggests that telemedicine could support outpatient and emergency care in pandemic situations. However, further studies using interventional methods are required to increase the generalizability of the findings.

Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain tumors in adults. Due to a low rate of survival in patients with these tumors, machine learning can help physicians for better decision-making. The aim of this paper is to develop a machine learning model for predicting the survival rate of patients with GBM based on clinical features and magnetic resonance imaging (MRI). Materials and Methods: The present investigation is an observational study conducted to predict the survival rate in patients with GBM in 12 months. Fifty-five patients who were registered in five Iranian Hospitals (Tehran) during 2012–2014 were selected in this study. Results: This study used Cox and C5.0 decision tree models based on clinical features and combined them with MRI. Accuracy, sensitivity, and specification parameters used to evaluate the models. The result of Cox and C5.0 for clinical feature was <32.73%, 22.5%, 45.83%>, <72.73%, 67.74%, 79.19%>, respectively; also, the result of Cox and C5.0 for both features was <60%, 48.58%, 75%>, <90.91%, 96.77%, 88.33%>, respectively. Conclusion: Using C5.0 decision tree model in both survival models including clinical features, both the imaging features and the clinical features as the covariates, shows additional predictive values and better results. The tumor width and Karnofsky performance status scores were determined as the most important parameters in the survival prediction of these types of patients.

Employees’ mental health addresses concerns in the technology industry phenomenon. Machine Learning (ML) approaches show promise in predicting mental health problems and identifying related factors. This study used three machine learning models on OSMI 2019 dataset: MLP, SVM, and Decision Tree. Five features are extracted by permutation ML’s method on the dataset. The results indicate that the models have been reasonably accurate. Moreover, they could effectively support predicting employee mental health comprehension in the technology industry.