International Journal of Telemedicine and Applications

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (). The second category, recurrent neural network (RNN), accounts for 10% (). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (). The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

Introduction. A significant proportion of diabetic patients in the United States do not present for annual dilated eye exams to monitor for signs of diabetic retinopathy (DR). The purpose of this study was to analyze the results of a statewide, multiclinic teleretina program designed to screen rural Arkansans for this sight-debilitating disease. Methods. Patients with diabetes seen at 10 primary care clinics across Arkansas were offered teleretinal-imaging services. Images were transmitted to the University of Arkansas for Medical Sciences’ (UAMS) Harvey and Bernice Jones Eye Institute (JEI) for grading and recommendations for further treatment. Results. From February 2019 to May 2022, 668 patients underwent imaging; 645 images were deemed of sufficient quality to generate an interpretation. 541 patients had no evidence of DR, while 104 patients had some evidence of DR. 587 patients had no evidence of maculopathy, while 58 patients had some evidence of maculopathy on imaging. 246 patients had other pathology evident on imaging, with the most common being hypertensive retinopathy, glaucoma suspects, and cataracts. Discussion. In a rural, primary care setting, the JEI teleretina program identifies DR and other nondiabetic ocular pathologies, allowing for an appropriate triage for eye care for patients in a predominantly rural state.

Background. Advances in information and communication technology have led to telemedicine applications that could support paramedics in the prehospital field. In an effort to optimise the available resources like prehospital emergency physicians (PHP), the State Health Services of a Swiss state decided to launch a pilot study on the feasibility of using telemedicine in the prehospital emergency setting. Objective. The primary objective was to measure the number of missions completed without technical problems with remote PHP support through telemedicine (tele-PHP). The secondary objectives were to evaluate the safety of this protocol and to describe the actions and decisions that clinicians can make by using tele-PHP. Methods. This was a prospective observational pilot study on all missions involving the dispatch of ground PHP or tele-PHP. The severity score, dispatch criteria, actions, and decisions made by ground PHP and tele-PHP were collected. Results. PHP were dispatched simultaneously with an ambulance on 478 occasions, including 68 (14%) situations that started directly with tele-PHP. Among those situations, three had to be transformed into on-site PHP missions after the on-site evaluation by paramedics. Fifteen missions were cancelled by paramedics once they were on site, and six missions encountered a connection issue. Forty-four PHP missions that were dispatched simultaneously with paramedics were completed by tele-PHP only without any connection problems. Paramedics and PHP estimated that actions or decisions were provided by PHP in 66% of the on-site PHP missions and 34% of the tele-PHP missions. Conclusions. This is the first experience of tele-PHP regarding PHP dispatch in Switzerland. Despite the small number of missions carried out, tele-PHP could be used for well-selected situations to reduce the need for a PHP on site.

In order to improve the efficiency of medical multimedia information sharing, this paper combines cloud computing technology and SOA (service-oriented architecture) technology to build a medical multimedia information sharing system. Building a medical information sharing platform requires integrating information resources stored in information systems of medical institutions and nonmedical information systems related to medical information and forming a huge resource pool. It is important to mine and analyze the information resources in the resource pool to realize the sharing and interaction of medical information. To this end, this paper proposes a gain-adaptive control algorithm with online adjustable parameters and investigates the extension of the mutual entropy optimization algorithm in the control domain and its integrated processing capability in the process of medical multimedia information processing. In addition, this paper constructs a medical multimedia information sharing and collaboration platform with medical multimedia information sharing and telemedicine as the core and verifies the effectiveness of the platform through experiments. The simulation results and comparison results with other systems prove that the system in this paper can realize fast data processing, retrieve and analyze massive data, and meet the demand of remote intelligent diagnosis under the premise of safety and stability. Meanwhile, the system in this paper can help hospitals achieve fast and accurate diagnosis, which has strong theoretical and practical values.

Mobile health as one of the new technologies can be a proper solution to support care provision for the elderly and provide personalized care for them. This study is aimed at reviewing the benefits and challenges of personalized mobile health (PMH) for elderly home care. With a systematic review methodology, 1895 records were retrieved by searching four databases. After removing duplicates, 1703 articles remained. Following full-text examination, 21 articles that met the inclusion criteria were studied in detail, and the output was presented in different tables. The results indicated that 25% of the challenges were related to privacy, cybersecurity, and data ownership (10%), technology (7.5%), and implementation (7.5%). The most frequent benefits were related to cost-saving (17.5%), nurse engagement improvement (10%), and caregiver stress reduction (7.5%). In general, the number of benefits in this study was slightly higher than the challenges, but in order to use PMH technologies, the challenges presented in this study must be carefully considered and a suitable solution must be adopted. Benefits can also be helpful in persuading individuals and health-care providers. This study shed light on those points that need to be highlighted for further work in order to convert the challenges toward benefits.

Introduction. Telemedicine was rapidly deployed at the onset of the COVID-19 pandemic. Little has been published on telemedicine in musculoskeletal care prior to the COVID-19 pandemic. This study is aimed at characterizing trends in telemedicine for musculoskeletal care preceding the COVID-19 pandemic. Methods. This retrospective study used insurance claims from the Truven MarketScan database. Musculoskeletal-specific outpatient visits from 2014 to 2018 were identified using the musculoskeletal major diagnostic category ICD-10 codes. Telemedicine visits were categorized using CPT codes and Healthcare Common Procedure Coding Systems. We described annual trends in telemedicine in the overall dataset and by diagnosis grouping. Multivariable logistic regression modeling estimated the association between patient-specific and telemedicine visit variables and telemedicine utilization. Results. There were 36,672 musculoskeletal-specific telemedicine visits identified (0.020% of all musculoskeletal visits). Overall, telemedicine utilization increased over the study period (0% in 2014 to 0.05% in 2018). Orthopedic surgeons had fewer telemedicine visits than primary care providers (OR 0.57, 95% CI 0.55-0.59). The proportion of unique patients utilizing telemedicine in 2018 was higher in the south (OR 2.28, 95% CI 2.19-2.38) and west (OR 5.58, 95% CI 5.36-5.81) compared to the northeast. Those with increased comorbidities and lower incomes and living in rural areas had lower rates of telemedicine utilization. Conclusions. From 2014 to 2018, there was an increase in telemedicine utilization for musculoskeletal visits, in part due to insurance reimbursement and telemedicine regulation. Despite this increase, the rates of telemedicine utilization are still lowest in some of the groups that could derive the most benefit from these services. Establishing this baseline is important for assessing how the roll-out of telemedicine during the pandemic impacted how/which patients and providers are utilizing telemedicine today.