Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM

Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity. A new network called the spatial convolution module (SCM) combines cross-channel and spatial information, and the convolution operations extract helpful features. The present model is evaluated using publicly accessible datasets ODIR, INSPIREVR, and VICAVR. We applied the augmentation to artificially increase the dataset of 1200 fundus images. The different HR severity levels of normal, mild, moderate, severe, and malignant are finally classified with the reduced time when compared to the existing models because in the proposed model, convolutional layers run only once on the input fundus images, which leads to a speedup and reduces the processing time in detecting the abnormalities in the vascular structure. According to the findings, the improved SVM had the highest detection and classification accuracy rate in the vessel classification with an accuracy of 98.99% and completed the task in 160.4 s. The ten-fold classification achieved the highest accuracy of 98.99%, i.e., 0.27 higher than the five-fold classification accuracy and the improved KNN classifier achieved an accuracy of 98.72%. When computation efficiency is a priority, the proposed model's ability to quickly recognize different HR severity levels is significant.

Self-reported diabetes mellitus among seafarers: occupational and sociodemographic predictors

BACKGROUND

Seafarers are at increased risk of diabetes due to their lifestyle and working conditions on board ships. There is, however, limited evidence regarding the magnitude of diabetes and its risk factors. In this study, we aimed to assess the prevalence of self-reported diabetes among seafarers on board ships and identify risk factors associated with it.

MATERIALS AND METHODS

A cross-sectional epidemiological survey was conducted among seafarers aboard ships between November and December 2022. The study enrolled a total of 4,500 seafarers aged 18 and older. Data were collected using anonymous, standardized questionnaires. The association between the outcome variable and the independent variables was assessed using binary logistic regression models.

RESULTS

In total, 2,986 participants were included in the study. The prevalence of self-reported diabetes among seafarers was found to be 8.2% (95% CI: 7.2-9.2). Self-reported diabetes prevalence among officers and non-officers was 7% and 9%, respectively. The mean age of study participants was 37.96 ± 10.22, while the mean age of participants with diabetes was 47.5 ± 9.46. Independent predictors of self-reported diabetes mellitus were age (51+ years) [adjusted odds ratio (AOR): 3.52, 95% confidence interval (CI): 1.46-8.95], rank (non-officer) [AOR: 1.65; 95% CI: 1.14-2.40], worksites (engine) (AOR: 2.08, 95% CI: 1.19-3.77), work experience (10-20 years) (AOR: 4.66, 95% CI: 2.33-10.05), work experience (21+ years) (AOR: 5.01, 95% CI: 2.32-11.55), working hours per week (57-70 hours) (AOR: 1.57, 95% CI: 1.08-2.31), working hours per week (71+ hours) (AOR: 1.80, 95% CI: 1.17-2.80), self-reported hypertension (AOR: 1.44, 95% CI: 1.03-1.99), overweight (AOR: 1.74; 95% CI: 1.24-2.47), and obesity (AOR: 2.93; 95% CI: 1.84-4.65).

CONCLUSIONS

This study revealed that one in twelve seafarers between the ages of 19 and 70 have self-reported diabetes. The present study identified significant risk factors associated with diabetes. Risk factor mitigation strategies aimed at high-risk groups should be implemented on board ships.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study

OBJECTIVES

High blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine.

DESIGN

A cross-sectional epidemiological study was employed.

SETTING

This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires.

PARTICIPANTS

This study involved 8125 seafarers aged 18-70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria.

OUTCOME MEASURES

We calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers' characteristics.

RESULTS

Among the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively.

CONCLUSION

A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships.

Automatic Detection and Classification of Diabetic Retinopathy Using the Improved Pooling Function in the Convolution Neural Network

Diabetic retinopathy (DR) is an eye disease associated with diabetes that can lead to blindness. Early diagnosis is critical to ensure that patients with diabetes are not affected by blindness. Deep learning plays an important role in diagnosing diabetes, reducing the human effort to diagnose and classify diabetic and non-diabetic patients. The main objective of this study was to provide an improved convolution neural network (CNN) model for automatic DR diagnosis from fundus images. The pooling function increases the receptive field of convolution kernels over layers. It reduces computational complexity and memory requirements because it reduces the resolution of feature maps while preserving the essential characteristics required for subsequent layer processing. In this study, an improved pooling function combined with an activation function in the ResNet-50 model was applied to the retina images in autonomous lesion detection with reduced loss and processing time. The improved ResNet-50 model was trained and tested over the two datasets (i.e., APTOS and Kaggle). The proposed model achieved an accuracy of 98.32% for APTOS and 98.71% for Kaggle datasets. It is proven that the proposed model has produced greater accuracy when compared to their state-of-the-art work in diagnosing DR with retinal fundus images.

The Use of Radio and Telemedicine by TMAS Centers in Provision of Medical Care to Seafarers: A Systematic Review

Objective: From medicine via radio to telemedicine, personalized medical care at sea has improved significantly over the years. Currently, very little research has been conducted on telemedicine services and tools at sea. This study aims to review real-time case studies of seafarers' personalized treatment via telemedical devices published in medical journals. Methods: A literature search was conducted using three libraries such as PubMed (Medline), Cumulative Index to Nursing and Allied Health Literature (CINAHL), BioMed Central, and Google Scholar. The Medical Subject Headings (MeSH) were used for information retrieval and document selection was conducted based on the guidelines of preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 flowchart. Selected articles were subjected to quality checks using the Newcastle-Ottawa scale (NOS). Results: The literature search produced 785 papers and documents. The selection was conducted in three stages such as selection, screening, and inclusion. After applying predefined inclusion and exclusion criteria, only three articles on real-time medical assistance with telemedical tools were identified. It is reported that medical attention is delivered to seafarers in real time thanks to advancements in telemedicine, satellite technology, and video conferencing. Conclusions: By improving the quality of medical care and reducing response times for medical emergencies at sea, lives have been saved. There are still several gaps despite these advancements. Medical assistance at sea should therefore be improved to address many of the still unsolved issues.

Systematic review on the current state of disaster preparation Simulation Exercises (SimEx)

INTRODUCTION

The simulation exercise (SimEx) simulates an emergency in which an elaboration or description of the response is applied. The purpose of these exercises is to validate and improve plans, procedures, and systems for responding to all hazards. The purpose of this study was to review disaster preparation exercises conducted by various national, non-government, and academic institutions.

METHODOLOGY

Several databases, including PubMed (Medline), Cumulative Index to Nursing and Allied Health Literature (CINAHL), BioMed Central, and Google Scholar, were used to review the literature. Information was retrieved using Medical Subject Headings (MeSH) and documents were selected according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). To assess the quality of the selected articles, the Newcastle-Ottawa Scale (NOS) technique was utilized.

RESULTS

A total of 29 papers were selected for final review based on PRISMA guidelines and the NOS quality assessment. Studies have shown that many forms of SimEx commonly used in disaster management including tabletop exercises, functional exercises, and full-scale exercises have their benefits and limitations. There is no doubt that SimEx is an excellent tool for improving disaster planning and response. It is still necessary to give SimEx programs a more rigorous evaluation and to standardize the processes more thoroughly.

CONCLUSIONS

Drills and training can be improved for disaster management, which will enable medical professionals to face the challenges of disaster management in the 21st century.

The Magnitude of Cardiovascular Disease Risk Factors in Seafarers from 1994 to 2021: A Systematic Review and Meta-Analysis

OBJECTIVES

The incidence of acute cardiac events is one of the main reasons for medical consultation, disembarkation, repatriation, and death among seafarers at sea. Managing cardiovascular risk factors, particularly those that can be modified, is the key to preventing cardiovascular disease. Therefore, this review estimates the pooled prevalence of major CVD risk factors among seafarers.

METHODS

We conducted a comprehensive search of studies published between 1994 and December 2021 in four international databases, namely PubMed/Medline, Scopus, Google Scholar, and Web of Science (WOS). Each study was evaluated for methodological quality using the Joanna Briggs Institute (JBI) critical appraisal tool for prevalence studies. The DerSimonian-Laird random-effects model with logit transformations was used to estimate the pooled prevalence of major CVD risk factors. The results were reported in accordance with the Preferred Items for Systematic Review and Meta-analysis (PRISMA) guidelines.

RESULTS

Out of all 1484 studies reviewed, 21 studies with 145,913 study participants met the eligibility criteria and were included in the meta-analysis. In the pooled analysis, the prevalence of smoking was found to be 40.14% (95% CI: 34.29 to 46.29%) with heterogeneity between studies (I² = 98%, p < 0.01). The prevalence of hypertension, overweight, obesity, diabetes mellitus, and alcohol consumption was 45.32%, 41.67%, 18.60%, 12.70%, and 38.58%, respectively. However, the sensitivity analysis after excluding studies showed a pooled prevalence of hypertension, overweight, obesity, and diabetes mellitus of 44.86%, 41.87%, 15.99%, and 16.84%, respectively. The subgroup analysis demonstrated that smoking prevalence among seafarers had decreased significantly after 2013.

CONCLUSION

This study demonstrated that CVD risk factors, particularly hypertension, overweight, smoking, alcohol consumption, and obesity, are prevalent among seafarers. These findings may serve as a guide for shipping companies and other responsible bodies in order to prevent CVD risk factors among seafarers. PROSPERO Registration: CRD42022300993.

Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function

Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely used in DR detection through the classification of blood vessel pixels from the remaining pixels. In this paper, an improved activation function was proposed for diagnosing DR from fundus images that automatically reduces loss and processing time. The DIARETDB0, DRIVE, CHASE, and Kaggle datasets were used to train and test the enhanced activation function in the different CNN models. The ResNet-152 model has the highest accuracy of 99.41% with the Kaggle dataset. This enhanced activation function is suitable for DR diagnosis from retinal fundus images.

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN

Melanoma is easily detectable by visual examination since it occurs on the skin’s surface. In melanomas, which are the most severe types of skin cancer, the cells that make melanin are affected. However, the lack of expert opinion increases the processing time and cost of computer-aided skin cancer detection. As such, we aimed to incorporate deep learning algorithms to conduct automatic melanoma detection from dermoscopic images. The fuzzy-based GrabCut-stacked convolutional neural networks (GC-SCNN) model was applied for image training. The image features extraction and lesion classification were performed on different publicly available datasets. The fuzzy GC-SCNN coupled with the support vector machines (SVM) produced 99.75% classification accuracy and 100% sensitivity and specificity, respectively. Additionally, model performance was compared with existing techniques and outcomes suggesting the proposed model could detect and classify the lesion segments with higher accuracy and lower processing time than other techniques.

A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review

Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining

Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers’ text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers’ Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.

The promise of telemedicine in Pakistan: A systematic review

BACKGROUND

Telemedicine offers the possibility of provision of medical assistance to remote patients, and it has great potential in developing countries like Pakistan. Telemedicine solves logistical barriers, gives support to weak health systems, and helps to establish worldwide networks of healthcare professionals. Because of the high implementation costs, it is not possible yet to adopt telehealth systems for low- and middle-income nations.

OBJECTIVE

To present a revision of region-based telemedical services in Pakistan.

METHODS

Libraries such as PubMed (Medline), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Scopus (EMBASE), and Google Scholar were used for document search. Newcastle-Ottawa Scale (NOS) was adopted to conduct study quality. Many of the studies (n-8) included in the review were of high quality as assessed through the Newcastle-Ottawa scale. Selected study characteristics were further analyzed based on different parameters such as publication year, sample size, study design, methods, motivation, and outcomes.

RESULTS

Search produced 955 articles and 11 items were ultimately selected to conduct the review. These studies were further characterized as region-based telemedicine implementation. Out of 11, eight studies were conducted in the urban region and three studies were conducted in the rural areas of Pakistan. Many studies produced evidence on telehealth interventions by smartphone services such as SMS, apps, and web-based telemedicine.

CONCLUSIONS

Telehealth interventions such as mHealth, eHealth, telemedicine, and telepharmacy in Pakistan were introduced starting from the last two decades. For obtaining the full benefits of these technologies, it is necessary that they but certainly need to become an integral part of Pakistan's current health infrastructure.

Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization

Diabetic retinopathy (DR) is one of the most important microvascular complications associated with diabetes mellitus. The early signs of DR are microaneurysms, which can lead to complete vision loss. The detection of DR at an early stage can help to avoid non-reversible blindness. To do this, we incorporated fuzzy logic techniques into digital image processing to conduct effective detection. The digital fundus images were segmented using particle swarm optimization to identify microaneurysms. The particle swarm optimization clustering combined the membership functions by grouping the high similarity data into clusters. Model testing was conducted on the publicly available dataset called DIARETDB0, and image segmentation was done by probability-based (PBPSO) clustering algorithms. Different fuzzy models were applied and the outcomes were compared with our probability discrete particle swarm optimization algorithm. The results revealed that the proposed PSO algorithm achieved an accuracy of 99.9% in the early detection of DR.

A descriptive epidemiological study of cardiovascular diseases among seafarers

BACKGROUND

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality among seafarers. This study aimed to evaluate CVDs distribution and differences, considering seafarers' rank and worksite groups.

MATERIALS AND METHODS

A descriptive epidemiological study was employed, and the analysis was based on the telemedical assistance data of the International Radio Medical Centre (C.I.R.M.) from 2010 to 2018. The age, gender, rank, and worksite variables were considered for the analysis. Chi-square or Fisher test was used to assess differences in CVD distribution between rank and worksite groups.

RESULTS

Cardiovascular diseases were the sixth leading cause of medical advice requests to C.I.R.M. Distribution of CVD significantly differed between officers and non-officers [x2 (5) = 17.308, p = 0.004]. Officers were often diagnosed with hypertensive CVD (46%), whereas non-officers were frequently diagnosed with ischaemic heart diseases (41%). There were no significant differences in the distribution of CVD diagnoses between worksite groups [x2 (10) = 12.863, p = 0.231].

CONCLUSIONS

The frequency of CVD is higher among non-officers and older seafarers who have been more often diagnosed with CVD. Specific interventions such as early diagnosis, regular monitoring, and physical training to reduce cardiovascular risk should be considered on board ships. Future studies should take into account the incidence rate of CVD between rank and worksite groups.

The effect of various in-vitro and ex-vivo parameters on irrigant flow and apical pressure using manual syringe needle irrigation: Systematic review

BACKGROUND

Fluid dynamics is a majorly neglected aspect to be studied in root canal irrigation. The fundamental rule to understand mechanics is to observe patterns of flow during the process. Thus, this work is conducted to do a systemic assessment of the in-vitro and ex-vivo based studies to evaluate the effect of various parameters on the irrigant flow and apical pressure on using a manual syringe needle for root canal irrigation.

METHODS

The literature search was conducted through libraries such as PubMed (Medline), CINAHL, Embase, Scopus and other hand literature from Google Scholar, the British medical library etc. The systematic review was reported following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. As they include studies that were in-vitro and ex-vivo based, the risk of bias of the selected articles was assessed using a customized tool based on the previous literature and parameters evaluated in the studies included.

RESULTS

The literature search resulted in 101 items of which 19 records were included in this review. Results reported that multiple factors and parameters were assessed to evaluate the flow and apical pressures on using manual syringe needle irrigation.

CONCLUSIONS

Present systematic review gives insights in-depth about the irrigation dynamics of manual syringe needle irrigation. Besides, it is inconclusive to compile a single factor or a single parameter contributing to the enhanced irrigant flow and least apical pressures.

Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer's disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

Web-based chatbot for Frequently Asked Queries (FAQ) in Hospitals

Objectives

Local hospitals are operated by the resigned association of patients as passive communication channels. The online hospital data related to the users' queries are not transparent and reliable. Therefore, it is crucial to have an intelligent web chatbot that manages user requests and provides quick access to local hospital information. In this paper, we present a framework and functionality of a chatbot developed using web technologies.

Methods

The bot engine was integrated by several machine learning approaches like gradient descent (GD) and natural language processing (NLP) algorithms. The trained data entered into the bot were split into mini-word batches, and the GD algorithm was applied sequentially on each mini-batch. The NLP methods involved in converting a word to its stem with a text result less readable by humans.

Results

The employed ML algorithms were successfully incorporated to manage the alternative synchronisation of text and voice messages.

Conclusions

The proposed bot can be a better solution for data extraction from local hospital which functioning as a good communication channel for both users and hospital staff and helpful in reducing the crowd.

Hypersensitivity reaction to hyaluronic acid dermal filler after the Pfizer vaccination against SARS-CoV-2

The ongoing vaccination program in the control of novel coronavirus (COVID-19)is providing an opportunity to public health authorities to emerge from the current pandemic crisis.Covid vaccination which protects from getting Covid  may have some minor side effects and in rare cases was accompanied by severe or immediate reactions. The development of hyaluronic acid soft tissue filler delayed inflammatory reactions following COVID-19 vaccination was reported.  Treatments based on hyaluronic acid represent one of the most largely used practice of aesthetic medicine. In this we present a case of a 38-year-old female patient with a confirmed hypersensitivity reaction after having the BNT162b2 vaccine (Pfizer, USA). This case indicates that an adequate patient knowledge prior to hyaluronic acid injections to avoid adverse effect of a delayed-type hypersensitivity response after COVID-19 vaccination is desirable.

A Two-Year Comparative Evaluation of Clinical Performance of a Nanohybrid Composite Resin to a Flowable Composite Resin

Objective: This prospective in vivo study aimed to compare the clinical behavior of a flowable composite resin (Genial Universal Flo, GC) and a nanohybrid universal composite resin (Tetric Evo Ceram, Ivoclar Vivadent) used in Class I and II direct esthetic restorations in posterior teeth. Methods: A total of 108 Class I and II direct restorations were performed in patients aged between 20 and 60 years. The originality of this study lies in the fact that both materials were placed in pairs, in the same clinical environment (i.e., the same patient and the same type of tooth). The evaluations were performed now of restoration and after 2-weeks, 6-, 12-, and 24-months intervals using clinical examination, clinical photographs, and radiological examination, according to modified USPHS criteria. Statistical analysis was performed using the Fisher exact test and chi-square analysis. Results: At baseline, the universal composite resin showed better esthetic properties such as surface luster, surface staining marginal staining. Both materials regressed significantly over time with no significant difference between groups. Conclusions: Both flowable and nanohybrid composite resins exhibit acceptable clinical performance. The present 24 months of evaluation of different composites showed that the G-ænial Universal Flo could be an effective esthetic material for posterior restoration. No significant difference between both materials over time concerning surface luster, surface staining, and marginal staining.

Second wave of COVID-19 in Italy: Preliminary estimation of reproduction number and cumulative case projections

The second wave of a novel coronavirus in Italy has caused 247,369 new cases and 1782 deaths only in October 2020. This significantly alarming infectious disease controlling board to impose again mitigation measures for controlling the epidemic growth. In this paper, we estimate the latest COVID-19 reproduction number (R_0) and project the epidemic size for the future 45 days. The R_0 value has calculated as 2.83 (95% CI: 1.5-4.2) and the cumulative incidences 100,015 (95% CI; 73,201-100,352), and daily incidences might be reached up to 15,012 (95% CI: 8234-16,197) respectively.

Tele-Neuropsychological Assessment of Alzheimer's Disease

Background: Because of the new pandemic caused by the novel coronavirus disease (COVID-19), the demand for telemedicine and telemonitoring solutions has been exponentially raised. Because of its special advantage to treat patients in an emergency without physical presence at a hospital via video conferencing, telemedicine has been used to overcome distance barriers and to improve access to special domains like neurology. In these pandemic times, telemedicine has been also employed as a support for the diagnosis and treatment of adult-onset dementia disorders including Alzheimer’s disease. Objective: In this study, we carried out a systematic literature analysis to clarify if the neuropsychological tests traditionally employed in face-to-face (FTF) contexts are reliable via telemedicine. Methods: A systematic literature search for the past 20 years (2001–2020) was carried out through the medical databases PubMed (Medline) and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). The quality assessment was conducted by adopting the Newcastle Ottawa Scale (NOS) and only studies with a NOS ≥ 7 were included in this review. Results: The Mini-Mental State Examination (MMSE) results do not differ when tests are administered in the traditional FTF modality or by videoconference, and only negligible minor changes in the scoring system were noticeable. Other neuropsychological tests used to support the diagnosis of AD and dementia such as the Token Test, the Comprehension of Words and Phrases (ACWP), the Controlled Oral Word Association Test showed high reliability between the two modalities considered. No differences in the reliability concerning the living setting or education of the subjects were reported. Conclusions: The MMSE, which is the main screening test for dementia, can be administered via telemedicine with minor adaptation in the scoring system. Telemedicine use for other neuropsychological tests also resulted in general reliability and enough accuracy. Cognitive assessment by videoconference is accepted and appreciated and therefore can be used for dementia diagnosis in case of difficulties to performing FTF assessments. This approach can be useful given a personalized medicine approach for the treatment of adult-onset dementia disorders.

The Benefits of Telemedicine in Personalized Prevention of Cardiovascular Diseases (CVD): A Systematic Review

INTRODUCTION

Adverse effects on personalized care and outcomes of cardiovascular diseases (CVD) could occur if health systems do not work in an efficient manner. The pandemic caused by COVID-19 has opened new perspectives for the execution and advancement of cardiovascular tests through telemedicine platforms.

OBJECTIVE

This study aimed to analyze the usefulness of telemedical systems for providing personal care in the prevention of CVD.

METHODS

A systematic review analysis was conducted on the literature available from libraries such as PubMed (Medline), Scopus (Embase), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Data available in the last 10 years (2011-2020) were also examined by PRISMA guidelines. The selected studies were divided into two categories: (1) benefits of telemedicine in CVD prevention, and (2) recent progress in telemedical services for personalized care of CVD.

RESULTS

The literature search produced 587 documents, and 19 articles were considered in this review. Results highlighted that the timely delivery of preventive care for CVD which can be implemented virtually can benefit and modify morbidity and mortality. This could also reduce the pressure on hospitals by decreasing acute CVD occurrence among the general population. The use of these technologies can also help to reduce access to hospitals and other medical devices when not necessary.

CONCLUSIONS

Telemedicine platforms can be used for regular checkups for CVD and contribute to preventing the occurrence of acute events and more in general the progression of CVD.

Critical reflections and solutions for health problems of Italian refugees

Background

In recent years, the flow of migrants and refugees to EU nations is recorded in high amounts. By travelling through ships or nonhygienic boats most of them tend to have a high amount of risk in getting health issues. However, getting proper healthcare is a funda-mental human right since refugees or migrants can actively contribute to the development of host countries and their native nationalities. Therefore, in this paper, we try to explore the common diseases that happened to migrants who travel to Italy in recent years.

Methods

We analyzed the Italian refugees' data provided by the Italian health ministry in the years 2013-16, and explore the key pathologies and their medical conditions. All the data correctly clas-sified and analyzed depending on their age, gender, and pathologies that they posses.

Results

From 2013 to 2016, in total, 455,912 refugees were trav-elled into Italy. 74% male, 12% female, and 14% of minor refugees were recorded during this period. In pathologies, rashes or skin dis-eases (scabies) were happening in often and after scabies pregnancy issues were observed on a considerable scale. Similarly, brain-related problems and surgeries were recorded at the lowest in number.

Conclusion

In 2015, about 12% of all workers in the EU nationali-ties were migrants. As of this, it is highly recommended in the provision of proper health facilities to them without making any judgments. This paper is aiming to provide prevention methodologies that are in high demand for health care of refugees and migrants.

Self-Reported Modifiable Risk Factors of Cardiovascular Disease among Seafarers: A Cross-Sectional Study of Prevalence and Clustering

BACKGROUND

Cardiovascular diseases (CVD) are the major cause of work-related mortality from diseases onboard ships in seafarers. CVD burden derives mainly from modifiable risk factors. To reduce the risk factors and the burden of CVD onboard ships in seafarers, it is important to understand the up-to-date prevalence of modifiable risk factors. The primary purpose of this study was to assess the prevalence and clustering of self-reported modifiable CVD risk factors among seafarers. We have also explored the association between socio-demographic and occupational characteristics and reported modifiable CVD risk factor clustering.

MATERIALS AND METHODS

A cross-sectional study was conducted among seafarers from November to December 2020 on board ships. In total, 8125 seafarers aged 18 to 70 were selected from 400 ships. Data were collected using a standardized and anonymous self-reported questionnaire. The prevalence value for categorical variables and mean differences for continuous variables were compared using chi-square and independent sample t-tests. Multinomial logistic regression models were performed to identify independent predictors for modifiable CVD risk factor clustering.

RESULTS

Out of a total of 8125 seafarers aged ≥18 years on selected vessels, 4648 seafarers volunteered to participate in the survey, with a response rate of 57.2%. Out of 4318 participants included in analysis, 44.7% and 55.3% were officers and non-officers, respectively. The prevalence of reported hypertension, diabetes, current smoking and overweight or obesity were 20.8%, 8.5%, 32.5%, and 44.7%, respectively. Overall, 40%, 20.9%, 6% and 1.3% of the study participants respectively had one, two, three and four modifiable CVD risk factors. Older age (51+ years) (odds ratio (OR): 3.92, 95% confidence interval (CI): 2.44-6.29), being non-officers (OR: 1.36, 95% CI: 1.09-1.70), job duration (10-20 years) (OR: 2.73, 95% CI: 2.09-3.57), job duration (21+ years) (OR: 2.60, 95% CI: 1.79-3.78), working 57-70 h per week (OR: 2.03, 95% CI: 1.65-2.49) and working 71+ h per week (OR: 3.08, 95% CI: 2.42-3.92) were independent predictors for at least two self-reported modifiable CVD risk factor clustering.

CONCLUSION

The results of our study demonstrate that more than four in six (68.5%) seafarers aged between 19 and 70 years have at least one of the modifiable CVD risk factors. Therefore, CVD prevention and modifiable risk factors reduction strategies targeting high-risk groups should be designed and implemented on board ships.

Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic

Objective: The largest pandemic in history, the COVID-19 pandemic, has been declared a doomsday globally. The second wave spreading worldwide has devastating consequences in every sector of life. Several measures to contain and curb its infection have forged significant challenges for the education community. With an estimated 1.6 billion learners, the closure of schools and other educational institutions has impacted more than 90% of students worldwide from the elementary to tertiary level. Methods: In a view to studying impacts on student's fraternity, this article aims at addressing alternative ways of educating-more specifically, online education-through the analysis of Google trends for the past year. The study analyzed the platforms of online teaching and learning systems that have been enabling remote learning, thereby limiting the impact on the education system. Thorough text analysis is performed on an existing dataset from Kaggle to retrieve insight on the clustering of words that are more often looked at during this pandemic to find the general patterns of their occurrence. Findings: The results show that the coronavirus patients are the most trending patterns in word search clustering, with the education system being at the control and preventive measures to bring equilibrium in the system of education. There has been significant growth in online platforms in the last year. Existing assets of educational establishments have effectively converted conventional education into new-age online education with the help of virtual classes and other key online tools in this continually fluctuating scholastic setting. The effective usage of teaching tools such as Microsoft Teams, Zoom, Google Meet, and WebEx are the most used online platforms for the conduction of classes, and whiteboard software tools and learning apps such as Vedantu, Udemy, Byju's, and Whitehat Junior have been big market players in the education system over the pandemic year, especially in India. Conclusions: The article helps to draw a holistic approach of ongoing online teaching-learning methods during the lockdown and also highlights changes that took place in the conventional education system amid the COVID pandemic to overcome the persisting disruption in academic activities and to ensure correct perception towards the online procedure as a normal course of action in the new educational system. To fill in the void of classroom learning and to minimize the virus spread over the last year, digital learning in various schools and colleges has been emphasized, leading to a significant increase in the usage of whiteboard software platforms.

A Review of Prolonged Post-COVID-19 Symptoms and Their Implications on Dental Management

The available data regarding the short and long-term consequences of COVID-19 is still insufficient. This narrative review aims to provide information on the prolonged COVID-19 symptoms in recovered patients and their implications during dental management. Additionally, this manuscript highlights the oral manifestations of COVID-19 and its management. A systematic search was conducted in PubMed, Embase, Cochrane Library and Web of Science databases, WHO and CDC websites, and grey literature was searched through Google Scholar. Clinical articles (clinical trials, case-reports, cohort, and cross-sectional studies) were included, reporting prolonged post-COVID-19 symptoms. Although COVID-19 is an infectious disease primarily affecting the lungs, its multi-organ involvement is responsible for several prolonged symptoms, including oral implications. In recovered patients with prolonged COVID-19 symptoms, considerations for providing dental treatment has to be made as they can present with assortment of symptoms. These prolonged post-COVID-19 symptoms can affect the delivery of the required dental treatment. Hence, the recommendations proposed in this narrative review can be a useful starting point to aid dental teams providing adequate care for such recovered patients.

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88-87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

Estimating the prevalence of stress among Indian students during the COVID-19 pandemic: A cross-sectional study from India

OBJECTIVE

Since December 2019, the COVID-19 pandemic has posed a substantial threat with its associated high mortality, infection, and risk of psychological stress. A large number of students are affected because of a prolonged break from academic activities and staying at home. The focus of this study is to understand the stress levels of Indian students, any psychological imbalances, and their major hurdles during the COVID-19 lockdown.

METHODS

Using a snowball sampling method, an online survey of the Perceived Stress Scale (PSS) was conducted on students across India. Along with their demographic details, the participants also reported their study patterns and challenges during their confinement period. The statistical scores for the responses were calculated and the demographic variables analysed. The levels indicated by the PSS were compared, and variance and regression analyses were performed.

RESULTS

We observed that students were generally stressed during lockdown and the pandemic. Females (mean = 3.03) were more stressed than males (mean = 2.61) as they were constantly under pressure because of stressful life events (OR = 0.752, 95% CI = 2.425-310.642) and apprehensive about their studies (RII = 0.67, OR = 2.168, 95% CI = 0.332-6.691).

CONCLUSION

During the pandemic, students' mental health needs to be continually monitored as they are stressed owing to fear as well as about their studies and future careers.

Incidence of occupational injuries and diseases among seafarers: a descriptive epidemiological study based on contacts from onboard ships to the Italian Telemedical Maritime Assistance Service in Rome, Italy

OBJECTIVES

Workers at sea have high mortality, injuries and illnesses and work in a hazardous environment compared to ashore workers. The present study was designed to measure the incidence of occupational injuries and diseases among seafarers and quantify the contribution of differences in rank and job onboard on seafarers' diseases and injuries rates.

DESIGN

Descriptive epidemiological study.

SETTING AND PARTICIPANTS

This study's data were based on contacts (n=423) for medical requests from Compagnie Maritime d'Affrètement/Compagnie Générale Maritime (CMA-CGM) container ships to the Italian Telemedical Maritime Assistance Service in Rome from 2016 to 2019, supplemented by data on the estimated total at-risk seafarer population on container ships (n=13 475) over the study period.

OUTCOME MEASURES

Distribution of injuries by anatomic location and types of diseases across seafarers' ranks and worksites. We determined the incidence rate and incidence rate ratio (IRR) with a 95% CI.

RESULTS

The total disease rate was 25 per 1000 seafarer-years, and the overall injury rate was 6.31 per 1000 seafarer-years over the 4 years study period. Non-officers were more likely than officers to have reported gastrointestinal (IRR 2.12, 95% CI 1.13 to 4.26), dermatological (IRR 3.66, 95% CI 1.27 to 14.42) and musculoskeletal (IRR 2.25, 95% CI 1.11 to 5.05) disorders onboard container ships. Deck workers were more likely than engine workers to be injured in the wrist and hand (IRR 3.25, 95% CI 1.19 to 10.23).

CONCLUSIONS

Rates of reported injury and disease were significantly higher among non- officers than officers; thus, this study suggests the need for rank-specific preventative measures. Future studies should consider risk factors for injury and disease among seafarers in order to propose further preventive measures.

Assessment of Awareness and Knowledge on Novel Coronavirus (COVID-19) Pandemic among Seafarers

Background: The ongoing pandemic due to the novel coronavirus (COVID-19) is becoming a serious global threat. Experts suggest that the infection can be controlled by immediate prevention measures. Sailing is one of the occupational categories more vulnerable to this virus outbreak due to the proximity of the working conditions. Objective: Awareness and knowledge assessments of seafarers towards the current epidemic is mandatory to understand the effectiveness and success of the infection control measures adopted by shipping companies. Methods: In this study, we presented an online questionnaire survey to determine the knowledge levels of COVID-19 among seafarers. The data were collected by self-reported survey, and analysis was done by the analysis of variance (ANOVA). The t-test was used to understand the knowledge attitude differences to COVID-19 among different occupational groups of seafarers, and the p-value ≤ of 0.05 was considered statistically significant. Results: Among 1,458 responses received, 92.82% had a college or university degree. The results reported that the mean COVID-19 knowledge score was 5.82 (standard deviation = 0.51, range 0–6), and the overall correct percentage was 97%. There was a statistically significant difference between age groups (F (4, 1453) = 5.44, p < 0.001) and educational groups (F (4, 1453) = 1.52, p < 0.001). The knowledge score was not significantly different across the educational status of the participants (F (2, 1455) = 1.52, p = 0.220). Conclusions: The present study highlighted good knowledge and behaviours among sailors about COVID-19. However, shipping companies need to come up with new campaigns to hold optimistic practices and suitable guidelines on ships, including cruise boats, to keep sea workers always alert and collaborative in mitigating the spread of COVID-19.

Telemedical assistance at sea in the time of COVID-19 pandemic

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the aetiological factor of COVID-19 infection, poses problems in providing medical assistance at sea. Ships are in an isolated environment, and most of the merchant ships do not carry medical personnel or medical supplies. Telemedicine offers a real possibility to provide reasonable quality medical assistance to seagoing vessels. The fact that ships may touch ports in affected areas, the difficulties for seafarers to be assisted ashore due to quarantine measures and the crews' lack of turnover make medical assistance at sea difficult. This study has compared maritime telemedical assistance data before and during the COVID-19 pandemic to propose prevention measures.

MATERIALS AND METHODS

The study was based on the data from medical records of Centro Internazionale Radio Medico (C.I.R.M.) database of seafarers assisted from January 1 to June 30, in the years 2017-2020. The data were collected separately for each year. Age, sex, rank, and pathologies affecting the assisted seafarers were considered. Common signs of COVID-19 infection such as fever, cough, sore throat, shortness of breath, and other respiratory symptoms were analysed.

RESULTS

From January 1, 2017, to December 31, 2019, C.I.R.M. assisted 15,888 patients on board ships. During the first 6 months of the years under evaluation, C.I.R.M. assisted 2,419 patients in 2017, 2,444 patients in 2018, 2,694 patients in 2019, and 3,924 in 2020. The number of assisted cases almost doubled in the first 6 months (from January to June) of 2020 compared to the same period of the previous years. Gastrointestinal disorders, injuries/traumas, and dermatological pathologies were the first, second, and third most often reported causes of illness on board over the 4-year study period. A higher number of seafarers with fever, cough, sore throat, and shortness of breath were assisted during the COVID-19 pandemic than before the coronavirus outbreak. Medical requests for fever increased significantly during the COVID-19 pandemic compared to the same period from 2017 to 2019.

CONCLUSIONS

The requests for medical advice for fever, sore throat, and shortness of breath were significantly more common during the coronavirus epidemic. Close follow-up, regular health education on preventing coronavirus transmission, personal protective equipment, adequate environmental hygiene, and applying other standard precautions could help minimise the risk factors for the spread of COVID-19.

Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model

Purpose

As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data.

Design/methodology/approach

In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model.

Findings

Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995.

Originality/value

In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.

Designing of an Expert system for the management of Seafarer's health

In general merchant ships do not have medical facilities on board. When seafarer got sickness or accident, either ship captain or officers who are in charge will assist them, but these people do not have enough medical knowledge. To overcome this, we developed a Seafarer Health Expert System (SHES) that can facilitate telemedical services in an emergency. A comprehensive analysis of seafarers' medical issues that were conducted from medical records of patients assisted on board ships by the International Radio Medical Center (C.I.R.M.), Italy. Data mining techniques are involved to manage epidemiological data analysis in a two-phase setup. In the first phase, the common pathologies that occurred onboard were analyzed, later a detailed questionnaire for each medical problem was developed to provide precise symptomatic information to the onshore doctor. In this paper, we mainly highlighted the SHES framework, design flow, and functionality. Besides, nine designing policies and three actors with separate working panels were clearly described. The proposed system is easy and simple to operate for anyone of no computer experience and create medical requests for the fast delivery of symptomatic information to an onshore doctor.

Telemedicine Practice: Review of the Current Ethical and Legal Challenges

Background: Telemedicine involves medical practice and information and communications technology. It has been proven to be very effective for remote health care, especially in areas with poor provision of health facilities. However, implementation of these technologies is often hampered by various issues. Among these, ethical and legal concerns are some of the more complex and diverse ones. In this study, an analysis of scientific literature was carried out to identify the ethical and legal challenges of telemedicine. Materials and Methods: English literature, published between 2010 and 2019, was searched on PubMed, Scopus, and Web of Science by using keywords, including "Telemedicine," "Ethics," "Malpractice," "Telemedicine and Ethics," "Telemedicine and Informed consent," and "telemedicine and malpractice." Different types of articles were analyzed, including research articles, review articles, and qualitative studies. The abstracts were evaluated according to the selection criteria, using the Newcastle-Ottawa Scale criteria, and the final analysis led to the inclusion of 22 articles. Discussion: From the aforementioned sample, we analyzed elements that may be indicative of the efficacy of telemedicine in an adequate time frame. Ethical aspects such as informed consent, protection data, confidentiality, physician's malpractice, and liability and telemedicine regulations were considered. Conclusions: Our objective was to highlight the current status and identify what still needs to be implemented in telemedicine with respect to ethical and legal standards. Gaps emerged between current legislation, legislators, service providers, different medical services, and most importantly patient interaction with his/her data and the use of that data.

Barriers to Sustainable Telemedicine Implementation in Ethiopia: A Systematic Review

Background: Different studies showed that the use of telemedicine is effective in reducing hospital burden, suffering from patients, need of transports, hospital fear, save money and time, and increasing the quality of health care. However, the implementation of telemedicine countenances different challenges in developing countries generally and in Ethiopia, particularly. This review aims to evaluate barriers affecting sustainable telemedicine implementation in Ethiopia. Methods: PubMed (Medline), Google Scholar, Embase, and Scopus databases were searched between July 4, 2020 and July 28, 2020. Studies published between 2005 and June 30, 2020 were considered. Relevant articles were selected by reviewing keywords, titles, and abstracts. Out of 40 articles, 33 articles remained after removing duplicates. We finally analyzed 14 articles from the mentioned databases based on our eligibility criteria and identified different barriers. We followed the preferred reporting items for systematic review and meta-analyses (PRSIMA 2009) checklist for this review. Results: We identified 25 barriers through 14 articles and classified barriers into organizational, users, and staff and programmers' barriers. Accordingly, organizational, users, and staff and programmer barriers were 12 (48%), 7 (28%), and 6 (24%), respectively, with the frequency of occurrence through 14 articles. Cost, awareness, and resistance to change were the most frequently reported barriers among organizational, user, and staff and programmer barriers, respectively. Conclusions: Infrastructure and costs were the most frequently reported barriers, and staff resistance to change was also the critical factor in influencing the sustainable implementation of telemedicine in Ethiopia.

SARS-CoV-2 epidemic calculation in Italy by SEIR compartmental models

Purpose

After the identification of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Wuhan, China, a pandemic was widely spread worldwide. In Italy, about 240,000 people were infected because of this virus including 34,721 deaths until the end of June 2020. To control this new pandemic, epidemiologists recommend the enforcement of serious mitigation measures like country lockdown, contact tracing or testing, social distancing and self-isolation.

Design/methodology/approach

This paper presents the most popular epidemic model of susceptible (S), exposed (E), infected (I) and recovered (R) collectively called SEIR to understand the virus spreading among the Italian population.

Findings

Developed SEIR model explains the infection growth across Italy and presents epidemic rates after and before country lockdown. The results demonstrated that follow-up of strict measures such that country lockdown along with high testing is making Italy practically a pandemic-free country.

Originality/value

These models largely help to estimate and understand how an infectious agent spreads in a particular country and how individual factors can affect the dynamics. Further studies like classical SEIR modeling can improve the quality of data and implementation of this modeling could represent a novelty of epidemic models.

Tropical Conditions and Outbreak of COVID-19

Background: The new novel Coronavirus 2019 (nCOV-19 or COVID-19) has caused an unprecedented pandemic in humans. All nations have heightened their surveillances after the quick diagnosis of potential cases of the COVID-19.  Objectives: Recent statistics have mentioned that virus outbreak in tropical countries is relatively low compared to cold nations. To support this conclusion, we considered the six main tropical regions to investigate the pandemic distribution at the initial phase.  Methods: Chi-square test was applied to understand the correlation between outbreak and temperature changes. Significant probability P-value was set to P<0.01. P-values were calculated to both positive and death cases.  Results: Out of 1211562 infected cases, 41776 cases (3.45%) were registered at high-temperature countries (P<0.0001) and 1161786 cases (96.55%) at other countries like European countries or the USA. Moreover, only 1433 mortality cases (2.2%) happened, and the remaining 97.8% of mortality happened among other nations. Conclusion: Similar to other respiratory viruses like flu and influenza, there is a low outbreak of COVID-19 in tropical nations compared to the other countries. Apart from weather conditions, it is also recommended to follow the serious preventive measures imposed by governments to survive this novel epidemic.