Background: Low-resolution magnetic resonance imaging (MRI) has high imaging speed, but the image details cannot meet the needs of clinical diagnosis. More and more researchers are interested in neural network-based reconstruction methods. How to effectively process the super-resolution reconstruction of the low-resolution images has become highly valuable in clinical applications. Methods: We introduced Super-Resolution Convolution Neural Network (SRCNN) into the reconstruction of magnetic resonance images. The SRCNN consists of three layers, the image feature extraction layer, the nonlinear mapping layer, and the reconstruction layer. For the feature extraction layer, a multi-scale feature extraction (MFE) method was used to extract the features in different scales by involving three different levels of views, which is superior to the original feature extraction in views with fixed size. Compared with the original feature extraction only in fixed size views, we used three different levels of views to extract the features of different scales. This MFE could also be combined with residual learning to improve the performance of MRI super-resolution reconstruction. The proposed network is an end-to-end architecture. Therefore, no manual intervention or multi-stage calculation is required in practical applications. The structure of the network is extremely simple by omitting the fully connected layers and the pooling layers from traditional Convolution Neural Network. Results and Conclusions: After comparative experiments,the effectiveness of the MFE SRCNN-based network in super-resolution reconstruction of MR images has been greatly improved. The performance is significantly improved in terms of evaluation indexes peak signal-to-noise ratio and structural similarity index measure, and the detail recovery of images is also improved.

Background and Purpose: To characterize the global physician community's opinions on the use of digital tools for COVID-19 public health surveillance and self-surveillance. Materials and Methods: Cross-sectional, random, stratified survey done on Sermo, a physician networking platform, between September 9 and 15, 2020. We aimed to sample 1000 physicians divided among the USA, EU, and rest of the world. The survey questioned physicians on the risk-benefit ratio of digital tools, as well as matters of data privacy and trust. Statistical Analysis Used: Descriptive statistics examined physicians' characteristics and opinions by age group, gender, frontline status, and geographic region. ANOVA, t-test, and Chi-square tests with P < 0.05 were viewed as qualitatively different. As this was an exploratory study, we did not adjust for small cell sizes or multiplicity. We used JMP Pro 15 (SAS), as well as Protobi. Results: The survey was completed by 1004 physicians with a mean (standard deviation) age of 49.14 (12) years. Enthusiasm was highest for self-monitoring smartwatches (66%) and contact tracing apps (66%) and slightly lower (48–56%) for other tools. Trust was highest for health providers (68%) and lowest for technology companies (30%). Most respondents (69.8%) felt that loosening privacy standards to fight the pandemic would lead to misuse of privacy in the future. Conclusion: The survey provides foundational insights into how physicians think of surveillance.

Background: Telerehabilitation has been recommended as a potential solution to bridge service delivery gap, especially in geographically remote areas with shortage of healthcare personnel and lack of access to physical therapy. This study was aimed to develop and test the feasibility of a smartphone video-based exercise program (VBEP) for patients with knee osteoarthritis (OA). Methods: This two-phase study involved the development and feasibility testing stages. A three-round modified Delphi approach was employed in the development phase involving a panel of four experts and a patient with knee OA. Based on consensus, five types of exercises comprising seated knee flexion and extension, quadriceps isometric setting, quadriceps strengthening exercise, hamstring clenches, and wall squats were developed into a video-program for knee OA. 15 consenting patients with knee OA participated in the feasibility testing of the program after 2 weeks of utilization. Feasibility of the VBEP was assessed using system usability scale and user experience questionnaire, respectively. The quadruple visual analog scale was used to assess the pain intensity. Results: The mean age and pain intensity of the participants were 67.3 ± 6.4 years and 61.1 ± 10.6, respectively. User perceived usability of the VBEP was 77.1 ± 13.1 (out of 100) with a high usability rating of 86.7%. Pragmatic quality score, hedonic quality rating, attractiveness, and perspicuity were 2.2 (out of 3.0), 1.6 (out of 3.0), 2.4 (out of 3.0), and 3.0 (out of 3.0), respectively. Efficiency, dependability, stimulation, and novelty scores were 2.3 (out of 3.0), 1.8 (out of 3.0), 2.3 (out of 3.0), and 1.0 (out of 3.0), respectively. Conclusions: The VBEP for knee OA has high usability and quality rating, as well as good user experience, and it may be a feasible alternative platform for rehabilitation of patients with knee OA.