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In-flight medical incidents are becoming increasingly critical as passengers with diverse health profiles increase in the skies. In this paper, we reviewed how airlines, aviation authorities, and healthcare professionals respond to such emergencies. The analysis was focused on the strategies developed by the top ten airlines in the world by examining training in basic first aid, collaboration with ground-based medical support, and use of onboard medical equipment. Appropriate training of crew members, availability of adequate medical resources on board airplanes, and improved capabilities of dialogue between a flying plane and medical doctors on the ground will contribute to a positive outcome of the majority of medical issues on board airlines. In this respect, the adoption of advanced telemedicine solutions and the improvement of real-time teleconsultations between aircraft and ground-based professionals can represent the future of aviation medicine, offering more safety and peace of mind to passengers in case of medical problems during a flight. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Lung disease is a respiratory disease that poses a high risk to people worldwide and includes pneumonia and COVID-19. As such, quick and precise identification of lung disease is vital in medical treatment. Early detection and diagnosis can significantly reduce the life-threatening nature of lung diseases and improve the quality of life of human beings. Chest X-ray and computed tomography (CT) scan images are currently the best techniques to detect and diagnose lung infection. The increase in the chest X-ray or CT scan images at the time of training addresses the overfitting dilemma, and multi-class classification of lung diseases will deal with meaningful information and overfitting. Overfitting deteriorates the performance of the model and gives inaccurate results. This study reduces the overfitting issue and computational complexity by proposing a new enhanced kernel convolution function. Alongside an enhanced kernel convolution function, this study used convolution neural network (CNN) models to determine pneumonia and COVID-19. Each CNN model was applied to the collected dataset to extract the features and later applied these features as input to the classification models. This study shows that extracting deep features from the common layers of the CNN models increased the performance of the classification procedure. The multi-class classification improves the diagnostic performance, and the evaluation metrics improved significantly with the improved support vector machine (SVM). The best results were obtained using the improved SVM classifier fed with the features provided by CNN, and the success rate of the improved SVM was 99.8%. [ABSTRACT FROM AUTHOR]

Word sense disambiguation (WSD) is a process used to determine the most appropriate meaning of a word in a given contextual framework, particularly when the word is ambiguous. While WSD has been extensively studied for English, it remains a challenging problem for resource-scarce languages such as Hindi. Therefore, it is crucial to address ambiguity in Hindi to effectively and efficiently utilize it on the web for various applications such as machine translation, information retrieval, etc. The rich linguistic structure of Hindi, characterized by complex morphological variations and syntactic nuances, presents unique challenges in accurately determining the intended sense of a word within a given context. This review paper presents an overview of different approaches employed to resolve the ambiguity of Hindi words, including supervised, unsupervised, and knowledge-based methods. Additionally, the paper discusses applications, identifies open problems, presents conclusions, and suggests future research directions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Retinal blood vessels are affected by a variety of eye diseases, including hypertensive retinopathy (HR) and diabetic retinopathy (DR). A person with HR needs to be sure to check their eyes regularly, which requires the use of computer vision methods to analyze images of the back of the eye and help ophthalmologists automatically. Automated diagnostic systems are useful for diagnosing different retinal diseases for ophthalmologists and patients who need to establish an automated HR detection and classification system using retinal images. In this work, a sliding band filter was used to improve the back-of-the-eye images and small convex regions to develop an automated system for detecting and classifying HR gravity levels. An image classification with improved wolf optimization along Bayes algorithm was conducted. The current model was tested using the publicly available dataset, and its results were compared to existing models. The results mentioned that the model-improved Naïve Bayes model classified the different HR severity levels on the optimized features and produced a maximum accuracy of 100% while being compared to other classifiers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Agriculture is the main occupation across the world with a dependency on rainfall. Weather changes play a crucial role in crop yield and were used to predict the yield rate by considering precipitation, wind, temperature, and solar radiation. Accurate early crop yield prediction helps market pricing, planning labor, transport, and harvest organization. The main aim of this study is to predict crop yield accurately. The incorporation of deep learning models along with crop statistics can predict yield rates accurately. We proposed an improved optimizer function (IOF) to get an accurate prediction and implemented the proposed IOF with the long short-term memory (LSTM) model. Manual data was collected between 1901 and 2000 from local agricultural departments for training, and from 2001 to 2020 from government websites of Andhra Pradesh (India) for testing purposes. The proposed model is compared with eight standard methods of learning, and outcomes revealed that the training error is small with the proposed IOF as it handles the underfitting and overfitting issues. The performance metrics used to compare the loss after implementing the proposed IOF were r, RMSE, and MAE, and the achieved results are r of 0.48, RMSE of 2.19, and MAE of 25.4. The evaluation was performed between the predicted crop yield and the actual yield and was measured in RMSE (kg/ha). The results show that the proposed IOF in LSTM has the advantage of crop yield prediction with accurate prediction. The reduction of RMSE for the proposed model indicates that the proposed IOFLSTM can outperform the CNN, RNN, and LSTM in crop yield prediction. [ABSTRACT FROM AUTHOR]

Introduction: The important aspect of endodontic treatment is root canal irrigation. Although agitation systems proved to have enhanced irrigant wall interactions, syringe needle-based delivery systems still play a significant role during the preparatory phases of root canal treatment. The current study aimed to evaluate the time taken for the irrigant delivery during conventional syringe irrigation using different volume syringes in different root canal preparations. Materials and Methods: Two hundred extracted single-rooted premolars with approximately round canals were randomly instrumented to preferred apical preparation sizes using 0.4 tapered Hyflex-CM rotary Ni-Ti instruments and randomly divided as follows: Group I, instrumentation to size 40, 0.04 taper (n = 50); Group II, instrumentation to size 30, 0.04 taper (n = 50); Group III, instrumentation to size 25, 0.04 taper (n = 50); and Group IV, instrumentation to size 20, 0.04 taper (n = 50). Fifty operators were chosen for the experimentation. They were asked to irrigate the prepared specimens using different volume syringes. The entire irrigant delivery time was recorded by a head nurse using a stopwatch, and the values were calibrated and statistically analyzed. Results: Group I recorded significantly lesser time for irrigant delivery during manual syringe needle irrigation (P = 0.005) compared to other groups. Conclusion: Irrigation delivery time was less in 40/0.04 taper preparations, using 1 ml volume syringes. [ABSTRACT FROM AUTHOR]

The immune system can be compromised when humans inhale excessive cooling. Physical activity helps a person's immune system, and influenza seasonally affects immunity and respiratory tract illness when there is no physical activity during the day. Whenever people chill excessively, they become more susceptible to pathogens because they require more energy to maintain a healthy body temperature. There is no doubt that exercise improves the immune system and an individual's fitness. According to an individual's health history, lifestyle, and preferences, the physical activity framework also includes exercises to improve the immune system. This study developed a framework for predicting physical activity based on information about health status, preferences, calorie intake, race, and gender. Using information about comorbidities, regions, and exercise/eating habits, the proposed recommendation system recommends exercises based on the user's preferences. [ABSTRACT FROM AUTHOR]

Objective. This study is aimed at determining two main points. First, if the Canary System™ (CS), initially used to assess caries, can measure a decalcification depth of bleached enamel quantitatively, and second, whether or not whitening has a harmful effect on enamel. This device can be considered a useful tool in the clinical assessment of the progression of demineralization after bleaching. Materials and Methods. This study collected sixty human premolars that are in a good state recently extracted for orthodontic reason. To properly disinfect and preserve the premolars, they were stored in a saline solution and later in distilled water for a period of two weeks to allow the premolars to rehydrate. Later, 24 hours before the experiment, the premolars were introduced into a solution of artificial saliva to acquire back their minerals. The mineral content of the teeth was measured by the Canary System™ before bleaching. The teeth were bleached with 30% hydrogen peroxide (fläsh HP 30%), 30 min per week and for 3 consecutive weeks to simulate the conditions of strong bleaching in the clinic. The extent of demineralized enamel was measured by the Canary System™ at three points on the enamel surface of each tooth. The data were averaged for each application of the bleaching product. The demineralization extent of the teeth was measured by the Canary System™ before and after bleaching. The significance level was set at 0.05, and SPSS version 26 was used. The data were analyzed by using Wilcoxon's and Student's tests. Results. Mineral loss occurred after the first bleaching session; the Canary System™ detected a decalcification in the first bleaching session (532 ± 322 μm) compared to the other sessions (p ≤ 0.05), while no significant change was detected between the second and the third sessions (p > 0.05). Conclusion. Based on the findings of the present study, under in vitro conditions, it was possible to measure the demineralization extent of bleached enamel with the Canary System™. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Globally, smart cities, infrastructure, and transportation have led to a rise in vehicle numbers, resulting in an increasing number of problems. This includes problems such as air pollution, noise pollution, high energy consumption, and people's health. A viable solution to these problems is carpooling, which involves sharing vehicles between people going to the same location. As carpooling solutions become more popular, they need to be implemented efficiently. Data analytics can help people make informed decisions when selecting a ride (Car or Bus). We applied machine learning algorithms to select the desired ride (Car or Bus) and used feature ranking algorithms to identify the foremost traits for selecting the desired ride. Based on the performance evaluation metric, 11 classifiers were used for the experiment. In terms of selecting the desired ride, Random Forest performs best. Using ten-fold cross-validation, we obtained a sensitivity of 87.4%, a specificity of 73.7%, an accuracy of 81.0%, a sensitivity of 90.8%, a specificity of 77.6%, and an accuracy of 84.7% using leave-one-out cross-validation. To identify the most favorable characteristics of the Ride (Car or Bus), the recursive elimination of features algorithm was applied. By identifying the factors contributing to users' experience, the service providers will be able to rectify those factors to increase business. It has been determined that the weather can make or break the user experience. This model will be used to quantify and map intrinsic and extrinsic sentiments of the people and their interactions with locality, socio-economic conditions, climate, and environment. [ABSTRACT FROM AUTHOR]

The healing of the periapical tissues is crucial to the success of root canal treatment. The review studies effectively examine various endodontic root canal sealants in terms of periapical healing. This systematic review was formulated following the PRISMA 2020 guidelines and registered in the international prospective register of systematic reviews (PROSPERO) number-CRD42021239192. To find relevant articles, PubMed Central and Medline databases (until February 2022) were searched. Studies that evaluated healing following the application of different endodontic sealers were analysed. A primary outcome measure was the resolution of periapical lesions following the endodontic treatment. In vivo studies comparing radiographic treatment outcomes and articles with a minimum of 6-month follow-up were included. A total of 9 clinical trial studies that met all the inclusion criteria were included in the analysis. The overall risk of bias was high in four studies out of nine studies. Periapical lesions showed significant healing after endodontic treatment regardless of sealer type, although bioceramic and bioactive sealers had shown better results. [ABSTRACT FROM AUTHOR]

Medical training is a foundation on which better health care quality has been built. Freshly graduated doctors have required a good knowledge of practical competencies, which demands the importance of medical training activities. As of this, we propose a methodology to discover a process model for identifying the sequence of medical training activities that had implemented in the installation of a Central Venous Catheter (CVC) with the ultrasound technique. A dataset with twenty medical video recordings were composed with events in the CVC installation. To develop the process model, the adoption of process mining techniques of infrequent Inductive Miner (iIM) with a noise threshold value of 0.3 had done. A combination of parallel and sequential events of the process model was developed. Besides, process conformance was validated with replay fitness value about 61.1%, and it provided evidence that four activities were not correctly fit in the process model. The present study can assist upcoming doctors involved in CVCs surgery by providing continuous training and feedback on better patient care. [ABSTRACT FROM AUTHOR]

The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority of people use online consultations for drug recommendations for all types of health issues. Emergencies such as pandemics, floods, or cyclones can be helped by the medical recommender system. In the era of machine learning (ML), recommender systems produce more accurate, quick, and reliable clinical predictions with minimal costs. As a result, these systems maintain better performance, integrity, and privacy of patient data in the decision-making process and provide precise information at any time. Therefore, we present drug recommender systems with a stacked artificial neural network (ANN) model to improve the fairness and safety of treatment for infectious diseases. To reduce side effects, drugs are recommended based on a patient's previous health profile, lifestyle, and habits. The proposed system produced results with 97.5% accuracy. A system such as this could be useful in recommending safe medicines to patients, especially during health emergencies. [ABSTRACT FROM AUTHOR]

Background: The availability of better healthcare services is critical for onboard seafarers. The development of expert systems can help ships with limited medical facilities, which allow the shipside doctors to properly refer symptoms to remote doctors. This allows clinicians to make a correct diagnosis from there, which leads to proper treatment. A software named Marine Doctor (M Doc) has been developed by incorporating computing technologies to address this objective. Methods: With the help of Information and Communication Technology (ICT) this application can support the provision of appropriate medical assistance to seafarers. The system was developed with Python Tkinter (frontend) and PHP (backend) languages. MySQL was used as a server database. Results: Seafarers can use M Doc to benefit from medical advice that can reduce complications due to misdiagnosis and help doctors to make better-informed decisions. By automatically collecting appropriate sequences of symptoms, doctors will be able to generate proper information for referral of patient symptoms and subsequent advice based on the data. Conclusions: Technology that supports experts on board ships in better interacting with Telemedical Maritime Assistance Services (TMAS) could define the future of medical assistance at sea. [ABSTRACT FROM AUTHOR]

An acknowledgment of feedback is extremely helpful in medical training, as it may improve student skill development and provide accurate, unbiased feedback. Data are generated by hundreds of complicated and variable processes within healthcare including treatments, lab results, and internal logistics. Additionally, it is crucial to analyze medical training data to improve operational processes and eliminate bottlenecks. Therefore, the use of process mining (PM) along with conformance checking allows healthcare trainees to gain knowledge about instructor training. Researchers find it challenging to analyze the conformance between observations from event logs and predictions from models with artifacts from the training process. To address this conformance check, we modeled student activities and performance patterns in the training of Central Venous Catheter (CVC) installation. This work aims to provide medical trainees with activities with easy and interpretable outcomes. The two independent techniques for mining process models were fuzzy (i.e., for visualizing major activities) and inductive (i.e., for conformance checking at low threshold noise levels). A set of 20 discrete activity traces was used to validate conformance checks. Results show that 97.8% of the fitness of the model and the movement of the model occurred among the nine activities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor impairment, as well as tremors, stiffness, and rigidity. Besides the typical motor symptomatology, some Parkinsonians experience non-motor symptoms such as hyposmia, constipation, urinary dysfunction, orthostatic hypotension, memory loss, depression, pain, and sleep disturbances. The correct diagnosis of PD cannot be easy since there is no standard objective approach to it. After the incorporation of machine learning (ML) algorithms in medical diagnoses, the accuracy of disease predictions has improved. In this work, we have used three deep-learning-type cascaded neural network models based on the audial voice features of PD patients, called Recurrent Neural Networks (RNN), Multilayer Perception (MLP), and Long Short-Term Memory (LSTM), to estimate the accuracy of PD diagnosis. A performance comparison between the three models was performed on a sample of the subjects' voice biomarkers. Experimental outcomes suggested that the LSTM model outperforms others with 99% accuracy. This study has also presented loss function curves on the relevance of good-fitting models to the detection of neurodegenerative diseases such as PD. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

As a new concept in urban development, smart cities are characterized primarily by their mobility. To solve these problems, it became necessary to develop an intelligent system. Using the Advanced Saliency Detection Method and an Efficient Features Extraction Model, the proposed work is aimed at detecting vacant outdoor parking lots. Experimental work has been conducted using the publicly available "PKLot" dataset, which consists of 695,899 segmented images. Under different weather conditions, the images were taken from three different camera locations in two different parking lots in Brazil, including sunny, cloudy, and rainy days. The experimental results mentioned that the hybrid feature extraction model enhanced the performance of parking detection systems. Using three different datasets, PUCPR, UFPR04, and UFPR05, we obtain an accuracy of 99.93%, 99.89%, and 99.87%. This is clear that the hybrid feature extraction model with the PUCPR dataset has produced the highest accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]