International Journal of Telemedicine and Applications最新文献

The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated coronavirus disease (COVID-19) has caused widespread public health concerns. Despite huge efforts to contain the disease spread, it is still on the rise because of the community spread pattern of this infection. In order to prevent the community spread, a nationwide lockdown was implemented, due to which many restrictions were imposed on movements of citizens within the country. Since the dental professionals were at the forefront of acquiring the infection, the majority of the dental clinics were shut for routine dental procedures. Only emergency treatment was provided to the patients. However, due to restrictions in movement, it was difficult for the patients to visit the clinics for routine check-ups. This was overcome by the advancements in technology which has a major impact on medicine. Due to increased usage of smartphones and related software applications, the clinical data exchange was facilitated between patients and clinicians which has been termed as "teledentistry." Teledentistry is a combination of telecommunications and dentistry, involving the exchange of clinical information and images for dental consultation and treatment planning. This technology served as a boon for the dentists to manage dental emergencies during the lockdown period. This narrative review discusses teledentistry and its applications in general and specialty dental practice amidst the COVID-19 lockdown.

The objective of this study was to design the screens of a future gamified mobile application for self-management of type 1 diabetes in children based on the opinion of caregivers at the King Fahad Hospital Diabetes Center, Saudi Arabia. To achieve this objective, a questionnaire was designed and distributed among 100 caregivers through face-to-face communication and social media using a Google Forms link. 65% of the participants met the inclusion criteria. The main result of this study was the design of 13 screens of a gamified application for self-management of type 1 diabetes in children from Saudi Arabia. The key features of the screens were caring for a character; using a challenging friend; inclusion of points, level, and leaderboard as rewarding principles; use of reminders and notifications for doctor's appointments, insulin injection times, blood glucose readings; and tips for improving medication adherence, increasing blood glucose readings, supporting physical activities, and adopting healthy eating habits. It can be concluded that the practical implementation of the screens in a future mobile application can motivate children with type 1 diabetes to improve eating habits, physical exercise, and cognitive, emotional, and social behaviors to maintain a stable state of health. Also, the content of the designed screens can help to monitor blood glucose readings and comply with medication treatment. The designed screens are adapted to the Arab culture.

Background: The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects.

Purpose: The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.

Aging in place is a notion which supports the independent living of older adults at their own place of residence for as long as possible. To support this alternative living which can be in contrast to various other types of assisted living options, modes of monitoring technology need to be explored and studied in order to determine a balance between the preservation of privacy and adequacy of sensed information for better estimation and visualization of movements and activities. In this paper, we explore such monitoring paradigm on how a network of RGB-D sensors can be utilized for this purpose. This type of sensor offers both visual and depth sensing modalities from the scene where the information can be fused and coded for better protection of privacy. For this purpose, we introduce the novel notion of passive observer. This observer is only triggered by detecting the absence of movements of older adults in the scene. This is accomplished by classifying and localizing objects in the monitoring scene from both before and after the detection of movements. A deep learning tool is utilized for visual classification of known objects in the physical scene followed by virtual reality reconstructing of the scene where the shape and location of objects are recreated. Such reconstruction can be used as a visual summary in order to identify objects which were handled by an older adult in-between observation. The simplified virtual scene can be used, for example, by caregivers or monitoring personnel in order to assist in detecting any anomalies. This virtual visualization can offer a high level of privacy protection without having any direct visual access to the monitoring scene. In addition, using the scene graph representation, an automatic decision-making tool is proposed where spatial relationships between the objects can be used to estimate the expected activities. The results of this paper are demonstrated through two case studies.

This paper describes the design and implementation of a software system to improve the management of diabetes using a machine learning approach and to demonstrate and evaluate its effectiveness in controlling diabetes. The proposed approach for this management system handles the various factors that affect the health of people with diabetes by combining multiple artificial intelligence algorithms. The proposed framework factors the diabetes management problem into subgoals: building a Tensorflow neural network model for food classification; thus, it allows users to upload an image to determine if a meal is recommended for consumption; implementing K-Nearest Neighbour (KNN) algorithm to recommend meals; using cognitive sciences to build a diabetes question and answer chatbot; tracking user activity, user geolocation, and generating pdfs of logged blood sugar readings. The food recognition model was evaluated with cross-entropy metrics that support validation using Neural networks with a backpropagation algorithm. The model learned features of the images fed from local Ghanaian dishes with specific nutritional value and essence in managing diabetics and provided accurate image classification with given labels and corresponding accuracy. The model achieved specified goals by predicting with high accuracy, labels of new images. The food recognition and classification model achieved over 95% accuracy levels for specific calorie intakes. The performance of the meal recommender model and question and answer chatbot was tested with a designed cross-platform user-friendly interface using Cordova and Ionic Frameworks for software development for both mobile and web applications. The system recommended meals to meet the calorific needs of users successfully using KNN (with k = 5) and answered questions asked in a human-like way. The implemented system would solve the problem of managing activity, dieting recommendations, and medication notification of diabetics.

Introduction: False alarm reduction is an important challenge in self-care, whereas one of the most important false alarm causes in the cardiology domain is electrodes misplacements in ECG recordings, the main investigations to perform for early and pervasive detection of cardiovascular diseases. In this context, we present and assess a new method for electrode reversals identification for Mason-Likar based 3D ECG recording systems which are especially convenient to use in self-care and allow to achieve, as previously reported, high computerized ischemia detection accuracy.

Methods: We mathematically simulate the effect of the six pairwise reversals of the LA, RA, LL, and C2 electrodes on the three ECG leads I, II, and V2. Our approach then consists in performing serial comparisons of the newly recorded 3D ECG and of the six derived ECGs simulating an electrode reversal with a standard, 12-lead reference ECG by means of the CAVIAR software. We further use a scoring method to compare these analysis results and then apply a decision tree model to extract the most relevant measurements in a learning set of 121 patients recorded in ICU.

Results: The comparison of the seven sets of serial analysis results from the learning set resulted in the determination of a composite criteria involving four measurements of spatial orientation changes of QRS and T and providing a reversal identification accuracy of 100%. Almost the same results, with 99.99% of sensitivity and 100% of specificity, were obtained in two test sets from 90 patients, composed of 2098 and 2036 representative ECG beats respectively recorded during PTCA balloon inflation, a procedure which mimics ischemia, and before PTCA for control.

Conclusion: Personalized automatic detection of ECG electrode cable interchanges can reach almost the maximal accuracy of 100% in self-care, and can be performed in almost real time.

Background: Advances in information and communication technologies (ICTs) represent a growing platform for the expansion of healthcare related services, but there is little information on how generational differences might account for distinct patterns of use and interest for ICTs. Our study aims to achieve a better understanding on how generational cohorts might influence the use and preferences for ICTs among patients with obstructive lung diseases in Latin America.

Materials and methods: We conducted an anonymous cross-sectional survey-based study, involving 968 patients with obstructive lung diseases (OLD) in Latin America. Patients rated their frequency of use and preferences of ICTs through a modified version of the Michigan Questionnaire. Chi-square test for association and adjusted regression analyses were performed.

Results: Of all, 63.6% of participants had Internet access. Younger generations, in particular Generation Z and Millennials, had the highest rate of Internet access and smartphone ownership, as well as of overall frequency of ICT use. Web-based Internet was found to be the main source to seek information about the disease (36.9%) across all generational cohorts. Generation Z and Millennials presented the highest odds to be interested in using Twitter (OR 31.79 and 8.86) for receiving health-related information, and email (OR 4.87 and 4.86) as the preferred way to ask physicians information related to their disease through ICTs.

Conclusion: Generational cohorts influence the use and preferences for ICTs among patients with obstructive lung diseases. Younger generational cohorts were associated with higher access to the Internet and smartphone ownership, as well as higher interest for using ICTs to receive and ask for health-related information.

Smart clothing that can measure electrocardiogram (ECG) signals and monitor the health status of people meets the needs of our increasingly aging society. However, the conventional measurement of ECG signals is complicated and its electrodes can cause irritation to the skin, which makes the conventional measurement method unsuitable for applications in smart clothing. In this paper, a novel wearable measurement of ECG signals is proposed. There are only three ECG textile electrodes knitted into the fabric of smart clothing. The acquired ECG signals can be transmitted to a smartphone via Bluetooth, and they can also be sent out to a PC terminal by a smartphone via WiFi or Internet. To get more significant ECG signals, the ECG differential signal between two electrodes is calculated based on a spherical volume conductor model, and the best positions on the surface of a human body for two textile electrodes to measure ECG signals are simulated by using the body-surface potential mapping (BSPM) data. The results show that position 12 in the lower right and position 11 in the upper left of the human body are the best for the two electrodes to measure ECG signals, and the presented wearable measurement can obtain good performance when one person is under the conditions of sleeping and jogging.

Purpose: The study aims to describe a novel method of utilization of the eyeSmart EMR (electronic medical record) app. It describes the demographic distribution, clinical presentation, query requested by the vision technician, and management advised to the patients by using "eyeSmart EMR" app from the vision centers located across a three-tier eye care network in India.

Methods: This is a retrospective review of all patients who required a tele-ophthalmology consultation from January 2017 to August 2018. The demographic, clinical details, and the impact of teleophthalmology using eyeSmart app, in the vision centers of a three-tier eye care network, was analyzed in detail.

Results: A total of 15,001 tele-ophthalmology consults were included which is from January 2017 to August 2018. The mean age was 38 ± 19 years and male to female ratio was 1 : 2. Video calls were performed for 6191 (41.27%) consults and the impact was measured. Additional clinical information was received in 65.61% consults through video call. Medical management was advised in 47.07% of patients and 30.30% were referred to higher centers for medical intervention and 0.59% were referred for surgical intervention, 16.23% were prescribed glasses. No intervention required for 0.69% of cases. Hence nil intervention was advised.

Conclusion: The combination of using tablet and video calls with the help of eyeSmart EMR app is a novel method in teleophthalmology. It helps in connecting the patients at rural areas and the ophthalmologists in higher centers. The use of technology plays a vital role in the appropriate medical management of the patient.