Current Issue : October-December Volume : 2021 Issue Number : 4 Articles : 5 Articles
Background and Objective. Glaucomatous vision loss may be preceded by an enlargement of the cup-to-disc ratio (CDR). We propose to develop and validate an artificial-intelligence-based CDR grading system that may aid in effective glaucoma-suspect screening. Design, Setting, and Participants. 1546 disc-centered fundus images were selected, including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images were randomly selected from the Age-Related Eye Disease Study and Singapore Malay Eye Study to develop the system. First, a proprietary semiautomated software was used by an expert grader to quantify vertical CDR. &en, using CDR below 0.5 (nonsuspect) and CDR above 0.5 (glaucoma suspect), deep-learning architectures were used to train and test a binary classifier system. Measurements. &e binary classifier, with glaucoma suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Results. &e system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). Conclusions. Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings....
Telemedicine (TM) can revolutionize the impact of diabetic wound care management, along with tools for remote patient monitoring (RPM). There are no low-cost mobile RPM devices for TM technology to provide comprehensive (visual and physiological) clinical assessments. Here, a novel low-cost smartphone-based optical imaging device has been developed to provide physiological measurements of tissues in terms of hemoglobin concentration maps. The device (SmartPhone Oxygenation Tool—SPOT) constitutes an add-on optical module, a smartphone, and a custom app to automate data acquisition while syncing a multi-wavelength near-infrared light-emitting diode (LED) light source (690, 810, 830 nm). The optimal imaging conditions of the SPOT device were determined from signal-to-noise maps. A standard vascular occlusion test was performed in three control subjects to observe changes in hemoglobin concentration maps between rest, occlusion, and release time points on the dorsal of the hand. Hemoglobin concentration maps were compared with and without applying an image de-noising algorithm, single value decomposition. Statistical analysis demonstrated that the hemoglobin concentrations changed significantly across the three-time stamps. Ongoing efforts are in imaging diabetic foot ulcers using the SPOT device to assess its potential as a smart health device for physiological monitoring of wounds remotely....
In this paper, a novel medical knowledge graph in Chinese approach applied in smart healthcare based on IoT and WoT is presented, using deep neural networks combined with self-attention to generate medical knowledge graph to make it more convenient for performing disease diagnosis and providing treatment advisement. Although great success has been made in the medical knowledge graph in recent studies, the issue of comprehensive medical knowledge graph in Chinese appropriate for telemedicine or mobile devices have been ignored. In our study, it is a working theory which is based on semantic mobile computing and deep learning. When several experiments have been carried out, it is demonstrated that it has better performance in generating various types of medical knowledge graph in Chinese, which is similar to that of the state-of-the-art. Also, it works well in the accuracy and comprehensive, which is much higher and highly consisted with the predictions of the theoretical model. It proves to be inspiring and encouraging that our work involving studies of medical knowledge graph in Chinese, which can stimulate the smart healthcare development....
We surveyed three well-established store-and-forward telemedicine networks to identify any changes during the first half of 2020, which might have been due to the effect of the COVID-19 coronavirus pandemic on their telemedicine operations. The three networks all used the Collegium Telemedicus system. Various quantitative performance indicators, which included the numbers of referrals and the case-mix, were compared with their values in previous years. Two of the three networks surveyed (A and B) provided telemedicine services for any type of medical or surgical case, while the third (network C) handled only pediatric radiology cases. All networks operated in Africa, but networks A and C also provided services in other resource-constrained regions. Two of the networks (networks B and C) used local staff to submit referrals, while network A relied mainly on its expatriate staff. During the first half of 2020, the numbers of referrals received on network B increased substantially, while in contrast, the numbers of referrals on network A declined. All three networks had relatively stable referral rates during 2018 and 2019. All three networks delivered a service that was rated highly by the referrers. One network operated at relatively high efficiency compared to the other two, although it is not known if this is sustainable. The networks which were more reliant on local referrers saw little reduction—or even an increase—in submitted cases, while the network that had the most dependence on international staff saw a big fall in submitted cases. This was probably due to the effect of international travel restrictions on the deployment of its staff. We conclude that organizations wanting to build or expand their telemedicine services should consider deliberately empowering local providers as their referrers....
As medical cannabis is legalized, food safety management systems, including CBD (cannabidiol), have received attention from scientific and engineering perspectives. Observations attribute CBD changes in acidic environments and high temperatures to THC (tetrahydrocannabinol). The current research focuses on employing and optimizing 3D printers, specifically material extrusion additive manufacturing processes for telemedicine applications to safely and accurately deliver CBD-infused food. Soft meat is prepared by supercritical CO2 (SC-CO2) process and simultaneously infused with hemp oil for food printing. This study personalized the amount of CBD-infused food and analyzed its operating parameters based on a theoretical Hagen-Poiseuille equation and pressure drop. Head speed, direction change within a given time, pressure drops at tip or piston, the constant mass-flux in PTE (piston type extrusion), Vizo design (VD) with aesthetic elements, and head travel distance have been optimized. Between the University of Texas at El Paso in Texas, USA, and the Korea University in Seoul, Korea, repeated IoT system variable experiments through the web-cloud were limited to less than 1 min, including print time. The telemedicine system was first tried and successfully performed using CBD-infused foods. During this process, images, G-code, video, and text, including medical descriptions, were provided simultaneously with CBD-infused food....
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