Remote Diagnosis Research Articles

Incidence of Referable Retinal Disease in Diabetic Patients at a Primary Care Practice.

This work tests the feasibility of remote ophthalmic imaging to identify referable retinal abnormalities and assesses the effectiveness of color fundus photography (CFP) vs optical coherence tomography (OCT) for this purpose. This prospective, nonrandomized study included 633 patients with diabetes at Duke Primary Care. Undilated patients underwent screening with CFP and OCT camera (MaestroCare, Topcon). Images were graded independently for interpretability and the presence of predetermined retinal disease. Retinal disease was classified as diabetic retinopathy (DR) referable to a retina specialist or incidental findings referable to either a retina specialist or a general ophthalmologist, depending on severity. Mean (SD) age of screened patients was 66 (13) years, and 49% were women. The average glycated hemoglobin A1c level was 7.6 % (SD, 1.7%), and 30% of the patients were on insulin. The average duration of diabetes was 5.9 (SD, 7.3) years. Remote images from OCT were significantly more interpretable than CFP (98% vs 83%, respectively; P < .001). Referral rates were 9% for DR and 28% for incidental findings. Among patients with DR, OCT and CFP were helpful in 58% and 87% of cases, respectively (P < .001). Remote diagnosis of ophthalmic imaging at the point of service may allow for early identification of retinal disease and timely referral and treatment. Our approach showed that OCT had significantly better interpretability, while CFP was more helpful in identifying DR. These findings may be important when choosing the screening device in a specific context.

3D-Printed Portable Robotic Mobile Microscope for Remote Diagnosis of Global Health Diseases

Microscopy plays a crucial role in the diagnosis of numerous diseases. However, the need for trained microscopists and pathologists, the complexity of pathology, and the accessibility and affordability of the technology can hinder the provision of rapid and high-quality diagnoses and healthcare. In this work, we present an affordable, 3D-printed, portable, robotic, mobile-based slide scanning microscope. The proposed device is composed of electronic, mechanical, and optical modules operated via smartphone with a control app. The device is connected and fully integrated with a telemedicine web platform, where digitized microscopy images can be remotely visualized and analyzed. The robotic scanner, which has approximately 1-µm resolution, has been evaluated in two clinical scenarios with histology and stool samples. The results showed sufficient image quality for performing a proper diagnosis in all cases under study.

140 Patient Performed Thoracic Lung Ultrasound

Given expanding utilization of telemedicine for remote patient monitoring and improving portable ultrasound technology, a situation in which patients perform their own ultrasound may not be far in the future. In this study we aimed to evaluate the ability of patients to self-perform lung ultrasound (LUS) with minimal guidance. We hypothesized that with brief instruction, patients could obtain diagnostic ultrasound images of similar quality as those obtained by ultrasound experts. This was a prospective observational study of patients ≥18 years old presenting to a tertiary care emergency department. Patients watched a 5-minute instructional video describing a four-zone LUS protocol (right superior anterior, left superior anterior, right hemidiaphragm, left hemidiaphragm). Patients were surveyed on their comfort and willingness to independently perform the study protocol. Study sonographers repeated the scans using the same scanning protocol. Images were blindly reviewed and independently scored by two ultrasound fellowship-trained attending physicians who rated image quality on a Likert scale from 1-5; a score of 3 or more was deemed interpretable. Inter-rater reliability between the two raters was estimated using intraclass correlation coefficient (ICC). Group differences were compared using Wilcoxon-Mann-Whitney tests and chi- square tests. 32 English-speaking patients (50% female) of mean age 52.9 were enrolled. 94% were right-handed. 75% had prior computer experience or used technology regularly, and 94% had access to smart phones. The proportion of interpretable images were similar between the two groups except for the left hemidiaphragm (90% of provider-obtained images were interpretable vs. 45% of patient-performed images, P=0.002). Overall, the majority of patient-performed images were interpretable (66%). The mean of image scores was significantly higher for provider-obtained images, compared to patient-obtained images (P<0.05), except for images in the right hemidiaphragm, where the quality was similar (P=0.08). Inter-rater reliability was good (ICC = 0.78, 95% CI 0.72-0.83). Overall, patients reported high comfort with performing LUS [median score 4, interquartile range (IQR)4-5] and high willingness to perform LUS in the future (median score 4, IQR 4-5). With limited video-based instruction, patients can independently and comfortably obtain interpretable LUS images. However, patients had more difficulty in obtaining interpretable images in the left hemidiaphragm. With advents in telemedicine and portable ultrasound, patient-performed ultrasound could be explored as a means of home monitoring or remote diagnosis, but further research is needed.

Telemedicine Among Oral Medicine Practitioners During COVID-19 Pandemic and Its Future Impact on the Specialty.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic necessitated widespread changes in delivery of dental care. Telemedicine utilization increased overnight during this pandemic because of its ability to enhance access to oral healthcare, easily link to remote area, time management, low cost, etc. We aimed to identify the utilization of telemedicine among oral medicine practitioners in Saudi Arabia during COVID-19 pandemic and identify future impacts, potential challenges and barriers hindering the implementation of telemedicine.MethodsThe present cross-sectional electronically self-administered supplement-based survey Supplement Based Survey study was conducted among different professional levels of oral medicine involving consultants, specialists, and residents. The supplement includes demographic data, the current use of telemedicine, participant’s perspective on future implications, and potential barriers and limitations of telemedicine. Internal consistency was measured using Cronbach’s alpha (α) to assess how closely related a set of items in our study.ResultsOf the total (N = 75) oral healthcare professionals, 52% were male, 70.7% were Saudi, 45.3% were consultant, 42.7% had 1–5 years of experience, 82.7% were practicing in urban areas, 93.3% stated COVID-19 affected their clinics, consultation was the major reason for the use of telemedicine before (50.7%) and during (72%) COVID-19 outbreak, 57.3% were communicating with their patients, 97.33% had not experienced any cyber risk, and 45% patients were satisfied with the telemedicine service; our data showed a high level of homogeneity (α = 0.82). The poor knowledge and training on telemedicine technologies may substantially (p < 0.05) impact the future implications of telemedicine in oral medicine practice.ConclusionTelemedicine could be a useful adjunct for oral medicine practice, especially for remote diagnosis, consultation, referral, training and education. Our study emphasizes an urgent need to increase the awareness and education on how to utilize and practice telemedicine technologies among medical staff to maximize the efficiency.

Spectroscopic behavior of various materials in a GEO simulated environment

Artificial objects currently populating Earth orbital regimes can be distinguished by comparing remote observational data to that of optical measurements within the visible (VIS) and near infrared (NIR) spectral regions of materials obtained in the laboratory. Careful comparison of spectrally resolved observations with laboratory-based material irradiation experiments can provide a tool for remote diagnosis and anomaly resolution. However, the spectral signatures of many materials change as a function of exposure to radiation in geosynchronous Earth orbit (GEO). In order for remote characterization to be a viable characterization technique, the evolution of material optical properties (VIS-NIR) upon exposure to the GEO environment must be understood. To this end, we have studied the reflectance of several commonly used spacecraft materials including organic polymers, solar cell coverglasses, black thermal control paint, and carbon-carbon (C–C) matrix composite as a function of exposure to high energy electron irradiation in order to mimic the harsh environment of GEO. It was found that different classes of materials exhibit large variation in radiation stability resulting in the development of unique spectral characteristics.

Digital Pathology in Cameroon.

PURPOSECancer is becoming increasingly prevalent among the group of treatable diseases in African countries. There is a shortage of clinicians and pathologists available for cancer diagnosis and treatment. These limited resources must be efficiently used to maximize the number of patients treated. One of the critical factors in treatment efficiency is the correct and timely diagnosis of specimens by pathologists. However, there is currently a significant shortage of cancer care clinicians in Africa and an even more considerable shortage of pathologists. This article presents an example in which telepathology was used to mitigate the lack of pathologists in Cameroon.METHODSThe telepathology workaround was implemented in a district hospital based in Cameroon's Adamawa region, where a European surgeon provides cancer treatment. A small histology laboratory there is run by one histotechnologist who processes surgical biopsies into histology slides. As there are no pathologists on site, these slides are digitally scanned using a mobile phone and a whole slide imaging (WSI) scanner. The slides are then shared electronically with a volunteering pathologist in Europe who provides a diagnostic report.RESULTSFrom 2018 to July 2019, specimens for 101 patients were photographed through an iPhone connected to a microscope eyepiece producing several individual images per specimen. From July 2019 to December 2020, slides from 282 patients were scanned using WSI and digitally transmitted.CONCLUSIONWSI on hematoxylin and eosin histology slides for remote diagnosis can increase cancer treatment efficacy and reduce overtreatment of tumors clinically suspicious for malignancy in under-resourced countries with a lack of pathologists.

Assessing task understanding in remote ultrasound diagnosis via gesture analysis

Assessing task understanding in remote ultrasound diagnosis via gesture analysis

Non-Contact Methods for High-Voltage Insulation Equipment Diagnosis during Operation

The article describes a complex of non-contact methods for remote diagnosis of high-voltage insulators as well as the two-channel method for remote diagnostics of the operating state of high-voltage insulators, based on the registration of partial discharges by electromagnetic and acoustic sensors. The presented device allows visual inspection and searches for faulty high-voltage equipment and a remote non-contact method of recording high-intensity electric fields of industrial frequency and their spatial distribution based on the electro-optical effect. The scheme of using the system for monitoring and diagnosing the technical condition of high-voltage support insulators of open switchgear is described. The results of experimental studies confirm the possibility of industrial applicability of the proposed method for non-contact remote diagnostics of the state of high-voltage insulators under operating voltage.

MRI-based brain tumor segmentation using FPGA-accelerated neural network

BackgroundBrain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors.ResultsCompared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively.ConclusionWe quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

Research on Remote Monitoring and Fault Diagnosis System for Coal Mine Electromechanical Equipment

This article proposes an architecture of equipment remote monitoring and fault diagnosis system in view of the increasingly complex business logic and functional requirements of the current coal mine electromechanical equipment remote monitoring and fault diagnosis system. We established a loosely coupled Web service system by abstracting and dividing the business logic and functional requirements of the remote monitoring and fault diagnosis system at different levels and different granularities. At the same time, we use the fault diagnosis model of multi-scale residual network and attention mechanism algorithm to verify the effectiveness of the fault diagnosis system.

Teledentistry and its applications in paediatric dentistry: A literature review.

BackgroundDuring the unfortunate event of the COVID-19 pandemic, restrictions were placed on face-to-face interactions in dentistry to promote social distancing and reduce spread of virus. In order to provide dental services teledentistry was employed.ObjectiveThis article aims to review the literature and information available on the provision and utilization of teledentistry as a method to address oral health needs of paediatric patients.ResultsTeledentistry has been utilised in paediatric population for the purpose of oral health education and promotion, remote diagnosis and monitoring, and behaviour guidance. Studies involving paediatric population for aforementioned applications have shown that this practice strategy is beneficial to provide dental treatment in remote locations with little access to paediatric dental specialists, monitor patients between appointments, conduct remote diagnosis and screening programmes, promote oral health of children through dental education, and in pre-appointment behaviour guidance. This method was particularly useful during the current COVID-19 pandemic where dentists use their mobile phones, computer webcams for patient appointments while maintaining safe distance and avoid exposing themselves as well as patient to the virus.ConclusionTeledentistry can be a supplement to face-to-face methods of paediatric dental care, ultimately leading to better patient management. This technology can make a significant contribution in reducing the supply-demand gap of paediatric dental specialists in places where healthcare facilities are limited and ensure safety during the pandemic whilst providing dental care to paediatric patients. Further research is required for safe, effective and evidence-based use of teledentistry in the field of paediatric dentistry.

Background gamma radiation and plant photosynthesis

The idea of experiments is inspired by experimental data on the transmutation of isotopes in biological systems. Model particles, in our opinion, responsible for nuclear reactions can be hydrogen atoms in unusual states. Previously, we have experimentally shown that the formation of such hydrogen atoms is observed under hard ultraviolet radiation (they can be formed during photosynthesis in plants). In turn, these atoms, entering into nuclear reactions, can lead to gamma radiation. In this regard, it is possible to emit both ultraviolet and gamma radiation from plants. Two radiascan 701A dosimeters were used for experimental studies of gamma radiation from indoor plants in daylight and in the dark. The radiation from deciduous plants was observed in daylight and stopped in the dark. The relative value of the background gamma radiation of plants did not exceed 2% of the background level in the room. In the case of cactus, there is mainly absorption of background gamma radiation associated with a significant green mass of the plant and scattering and absorption of gamma quanta in the volume of the cactus. Cactus radiation can be observed if it exceeds the absorption of gamma quanta. The probability of such a situation depends on the extent to which the surface of the cactus is developed, intensive vegetation (flowering), and regular watering. For applied purposes, these radiations can be used for remote diagnosis of the state of plants.

Blockchain-Based Mutual Authentication Protocol with Privacy Protection in Telemedicine

Telemedicine builds a patient-centered remote diagnosis and continuous monitoring service system, which can effectively alleviate the current situation of difficult and expensive medical treatment. This paper focuses on mutual authentication with privacy protection in telemedicine. First, we propose a decentralized multi-authority attribute-based signature scheme. The signature scheme can be used to verify the signer’s attributes without leaking the identity information, thereby achieving privacy protection while authenticating. Combined with the proposed signature scheme, a blockchain-based mutual authentication protocol with privacy protection in telemedicine is designed. By this protocol, patients and telemedicine terminal can encrypt and store the medical data off-chain, and store the data digest and decentralized attribute-based signature on the blockchain. As a result, the confidentiality of the data and the authenticity of the source can be guaranteed. In addition, the nature of the blockchain and signature ensure that accurate liability determination can be made when medical disputes occur. Theoretical analysis shows that our protocol is secure and practical.

Privacy-Preserving Authentication Protocol for Wireless Body Area Networks in Healthcare Applications.

Mobile healthcare service has become increasingly popular thanks to the significant advances in the wireless body area networks (WBANs). It helps medical professionals to collect patient’s healthcare data remotely and provides remote medical diagnosis. Since the health data are privacy-related, they should provide services with privacy-preserving, which should consider security and privacy at the same time. Recently, some lightweight patient healthcare authentication protocols were proposed for WBANs. However, we observed that they are vulnerable to tracing attacks because the patient uses the same identifier in each session, which could leak privacy-related information on the patient. To defeat the weakness, this paper proposes a privacy-preserving authentication protocol for WBANs in healthcare service. The proposed protocol is only based on one-way hash function and with exclusive-or operation, which are lightweight operations than asymmetric cryptosystem operations. We performed two rigorous formal security proofs based on BAN logic and ProVerif tool. Furthermore, comparison results with the relevant protocols show that the proposed protocol achieves more privacy and security features than the other protocols and has suitable efficiency in computational and communicational concerns.

Analytical Review of Audiovisual Systems for Determining Personal Protective Equipment on a Person's Face

Since 2019 all countries of the world have faced the rapid spread of the pandemic caused by the COVID-19 coronavirus infection, the fight against which continues to the present day by the world community. Despite the obvious effectiveness of personal respiratory protection equipment against coronavirus infection, many people neglect the use of protective face masks in public places. Therefore, to control and timely identify violators of public health regulations, it is necessary to apply modern information technologies that will detect protective masks on people's faces using video and audio information. The article presents an analytical review of existing and developing intelligent information technologies for bimodal analysis of the voice and facial characteristics of a masked person. There are many studies on the topic of detecting masks from video images, and a significant number of cases containing images of faces both in and without masks obtained by various methods can also be found in the public access. Research and development aimed at detecting personal respiratory protection equipment by the acoustic characteristics of human speech is still quite small, since this direction began to develop only during the pandemic caused by the COVID-19 coronavirus infection. Existing systems allow to prevent the spread of coronavirus infection by recognizing the presence/absence of masks on the face, and these systems also help in remote diagnosis of COVID-19 by detecting the first symptoms of a viral infection by acoustic characteristics. However, to date, there is a number of unresolved problems in the field of automatic diagnosis of COVID-19 and the presence/absence of masks on people's faces. First of all, this is the low accuracy of detecting masks and coronavirus infection, which does not allow for performing automatic diagnosis without the presence of experts (medical personnel). Many systems are not able to operate in real time, which makes it impossible to control and monitor the wearing of protective masks in public places. Also, most of the existing systems cannot be built into a smartphone, so that users be able to diagnose the presence of coronavirus infection anywhere. Another major problem is the collection of data from patients infected with COVID-19, as many people do not agree to distribute confidential information.

Multi-level and relevance-based parallel clustering of massive data streams in smart manufacturing

Parallel implementations of incremental clustering have been provided to increase performances of data stream processing in smart factories, to enable real-time anomaly detection, remote diagnosis, condition-based monitoring of Cyber-Physical Systems. Incremental clustering algorithms iteratively extract and update over time clusters of data points (often denoted as micro-clusters) whose maximum number is bounded. However, the capability of controlling costs derived from the exploitation of computational resources on the distributed architecture is challenging to enable a sustainable processing of massive data streams. In this paper, we present a multi-level parallelization approach for clustering massive data streams based on an horizontal scaling platform for Big Data processing. In particular, the following levels are considered: (i) a first parallelization level is based on a multi-dimensional model with exploration facets used to perform a first, coarse-grained partition of data streams, according to a divide-and-conquer strategy; (ii) a second parallelization level is based on a buffering mechanism, that splits the data stream into portions of data points on which processing is performed in parallel; (iii) the third level of parallelization is defined over the set of micro-clusters that are generated and change over time. The approach is conceived for anomaly detection in smart manufacturing, where the concept of data relevance, defined in terms of distance from critical conditions of monitored systems, is used in order to force a stronger parallelization (and therefore higher resource usage) only when necessary, that is, when approaching to critical conditions. The scalability and efficiency of the approach are evaluated using a real dataset in a smart factory scenario. In particular, experiments demonstrated that when the maximum number of allowed micro-clusters decreases and the buffer size increases, parallelization based on buffering does not ensure good scalability. Additionally, as the number of features (that is, the complexity of data stream) increases, the parallelization based on buffering may present scalability issues. This paves the way to the advantages of tuning different parallelization levels according to the approach proposed in this paper.

ROTed: Random Oblivious Transfer for embedded devices

Oblivious Transfer (OT) is a fundamental primitive in cryptography, supporting protocols such as Multi-Party Computation and Private Set Intersection (PSI), that are used in applications like contact discovery, remote diagnosis and contact tracing. Due to its fundamental nature, it is utterly important that its execution is secure even if arbitrarily composed with other instances of the same, or other protocols. This property can be guaranteed by proving its security under the Universal Composability model. Herein, a 3-round Random Oblivious Transfer (ROT) protocol is proposed, which achieves high computational efficiency, in the Random Oracle Model. The security of the protocol is based on the Ring Learning With Errors assumption (for which no quantum solver is known). ROT is the basis for OT extensions and, thus, achieves wide applicability, without the overhead of compiling ROTs from OTs. Finally, the protocol is implemented in a server-class Intel processor and four application-class ARM processors, all with different architectures. The usage of vector instructions provides on average a 40% speedup. The implementation shows that our proposal is at least one order of magnitude faster than the state-of-the-art, and is suitable for a wide range of applications in embedded systems, IoT, desktop, and servers. From a memory footprint perspective, there is a small increase (16%) when compared to the state-of-the-art. This increase is marginal and should not prevent the usage of the proposed protocol in a multitude of devices. In sum, the proposal achieves up to 37k ROTs/s in an Intel server-class processor and up to 5k ROTs/s in an ARM application-class processor. A PSI application, using the proposed ROT, is up to 6.6 times faster than related art.

Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures.

(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

Intelligent Diagnosis of Equipment Health Based on IOT and Operation Large Data Analysis

Predictive maintenance integrates equipment condition monitoring, fault diagnosis, fault prediction, maintenance decision support and maintenance activities. Intelligent manufacturing upgrades need to match the synchronous improvement of predictive maintenance capabilities, and predictive maintenance is the basic guarantee for enterprises to achieve intelligent manufacturing. Take the equipment condition monitoring and diagnosis system applicate in process industry as an example, this paper proposes IOT and operation big data analysis to equipment fault monitoring, diagnosis and preventive maintenance. Under the three-layer system framework of perception layer, network layer and application layer, machine learning algorithm is applied to carry out data mining on the equipment operation big data, establish expert knowledge base, obtain diagnosis rules, and realize the intelligent and efficient management mode integrating online monitoring, remote monitoring, remote diagnosis, fault matching and identification. Based on IOT and operation large data analysis, equipment health intelligent diagnosis provides the basic guarantee for equipment intelligent operation and maintenance, which helping enterprise establish new equipment management, maintenance, inspection and repair system under the concept of predictive maintenance and proactive maintenance.

Recommendations for managing endodontic emergencies during coronavirus disease 2019 outbreak

The management of endodontic emergencies has been particularly challenging during the coronavirus disease 2019 (COVID-19) outbreak because of the possible generation of airborne particles and aerosols. The aim of this report was to contribute to the practice of endodontics by proposing a general protocol for the management of emergencies showing the rationale for remote diagnosis, clinical procedures, and the use of personal protective equipment and barriers at the dental office during the COVID-19 outbreak. A review of the literature was conducted up to May 2020 on relevant institutional sites, aiming to retrieve the best updated evidence. The reporting considered the Reporting Tool for Practice Guidelines in Health Care statement. Recommendations from Cochrane Oral Health, the American Dental Association, and the Centers for Disease Control and Prevention were included along with the American Association of Endodontists resources and scientific articles that addressed the issue. According to this article the proposed protocol could contribute to the management of endodontic emergencies at the dental office during the COVID-19 outbreak.