Attitudes of Medical Practitioners towards Collaboration with Dental Professionals during a Pandemic: A Past Experience and a Future alert

Aim: To determine the attitude of medical practitioners towards collaborating with dental professionals during a pandemic. Material(s) and Method(s): The present study is a cross sectional survey conducted among the medical practitioners of India. 2100 medical practitioners were randomly selected as study subjects. The data pertaining to their attitude toward collaboration with dental professionals were gathered using a self-administered questionnaire. Data was analyzed using descriptive studies. Result(s): Among the study subjects, 93% of the medical doctors said in the future if the pandemic occurs then they would feel contended if they were to be given the provision to be aided by a well-trained dental support team, 80% of them said they experienced high stress during the pandemic. When asked to specify the reason in case they have not approached the dentist during the pandemic, 89% said they did not ponder over the thought of taking help from the dentist. Conclusion(s): If a pandemic occurs in the future, the contribution of the dentist can be beyond dentistry provided we adopt and execute proper measures and plan them beforehand.Copyright © 2021 Muslim OT et al.

Impact of Stress During COVID-19 Pandemic

The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.

Chest Abnormality Detection from X-Rays Using Deep Learning

Coronavirus (COVID19) is a highly contagious virus which had already killed thousands of people and infected millions more throughout the world. One of the primary challenges that medical practitioners encounter in the realm of healthcare is correctly diagnosing patients conditions and infections. So far, the gold standard screening method RT-PCR test which has been designed to detect covid-19 which only has a positive rate ranging between 30 precent and 60 percent. As a result, a system that can accurately identify images and diagnose or anticipate diseases is needed. As a result, we set out to swiftly create a compact CNN architecture capable of recognizing COVID-19-infected individuals. Different CNN architectures are suggested in this paper to extract information from X-rays which further classified into Covid-19, pneumonia, or healthy. Here, we have used two datasets from publically available repositories that are Kaggle and Mendeley [1] [2]. To see how the size of datasets affects CNN performance, we train the suggested CNNs with both the original and enhanced datasets where datasets are splitted into ratios of 80:20 and 70:30 and the comparison is shown. Also suggested CNN model is compared with the five state-of-Art pre-Trained models (VGG-16, ResNet50, InceptionV3, EfficientNetB2, DenseNet121) with the same datasets and splitting ratios. we have also used Some visualization methods through which we can get an exact idea of how CNN functions and the explanation behind the network's decisions. This study suggests a model for classifying COVID-19 patients but makes no claims about medical diagnostic accuracy. © 2022 IEEE.

A Bicentric Study to Investigate the Impact of COVID-19 on Urological Patients

In December 2019, SARS-CoV-2 broke out, which raised great attention worldwide. In fact, it was essential to reorganize the management of economic, infrastructural and medical resources to deal with the inadequate preparation of medical practitioners for this emergency. It was evident that the global health, medical and scientific communities were not adequately prepared for this emergency, so during the pandemic. In this paper, data extracted from hospital discharge records of the Department of Urology of the A.O.R.N "Cardarelli” in Naples, Italy, were used. This work is an extension of a previous work, whose goal concerned how admission procedure in the Urology department of the "San Giovanni di Dio and Ruggi d'Aragona” hospital has been affected by COVID-19 pandemic. In this work we compare the results obtained for the patients of the University Hospital "San Giovanni di Dio and Ruggi d'Aragona” of Salerno and the patients of the A.O.R.N. "Antonio Cardarelli” of Naples (Italy). Data have been extracted from both hospitals discharge records of the Departments of Urology. Experimental analysis performed comparing pre-pandemic data with those collected during the epidemic showed an in-crease in the number of emergency hospitalizations and a decrease in planned pre-admission hospitalizations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Initial Stage Identification of COVID-19 Using Capsule Networks

Coronavirus is a pandemic that has kept us in great grief for the past few months. These days have created a devastating effect all through the world. As coronavirus has lot of similarities with other lung diseases, it becomes a challenging task for medical practitioners to identify the virus. A fast and robust system to identify the disease has been the need of the hour. In this chapter, we have used convolutional CapsNet for detecting COVID-19 disease using chest X-ray images. This design aims at obtaining fast and accurate diagnostic results. The proposed technique with less trainable parameters, COVID-CAPS, produced an accuracy of 87.5%, a sensitivity of 90%, a specificity of 95.8%, and an area under the curve (AUC) of 0.97. The main advantage of using CapsNet is that it can capture affine transformation in data that is a common scenario while dealing with real-world X-ray images. The CapsNet model is trained with normal data and tested with affine transformed data. The accuracy level obtained in the proposed method is comparatively much better along with having less learnable parameters and computational speed as compared to standard architectures such as ResNet, MobileNet, etc. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Concept-Based Review on Generative Adversarial Network for Generating Super Resolution Medical Image Using SWOT Analysis

Several alarming health challenges are urging medical experts and practitioners to research and develop new approaches to diagnose, detect and control the early spread of deadly diseases. One of the most challenging is Coronavirus Infection (Covid-19). Models have been proposed to detect and diagnose early infection of the virus to attain proper precautions against the Covid-19 virus. However, some researchers adopt parameter optimization to attain better accuracy on the Chest X-ray images of covid-19 and other related diseases. Hence, this research work adopts a hybridized cascaded feature extraction technique (Local Binary Pattern LBP and Histogram of Oriented Gradients HOG) and Convolutional Neural Network CNN for the deep learning classification model. The merging of LBP and HOG feature extraction significantly improved the performance level of the deep-learning CNN classifier. As a result, 95% accuracy, 92% precision, and 93% recall are attained by the proposed model. © 2022 IEEE.

Enhanced Chest X-Ray Classification Model for Covid-19 Patients Using HOG and LBP

Several alarming health challenges are urging medical experts and practitioners to research and develop new approaches to diagnose, detect and control the early spread of deadly diseases. One of the most challenging is Coronavirus Infection (Covid-19). Models have been proposed to detect and diagnose early infection of the virus to attain proper precautions against the Covid-19 virus. However, some researchers adopt parameter optimization to attain better accuracy on the Chest X-ray images of covid-19 and other related diseases. Hence, this research work adopts a hybridized cascaded feature extraction technique (Local Binary Pattern LBP and Histogram of Oriented Gradients HOG) and Convolutional Neural Network (CNN) for the deep learning classification model. The merging of LBP and HOG feature extraction significantly improved the performance level of the deep-learning CNN classifier. As a result, 95% accuracy, 92% precision, and 93% recall are attained by the proposed model. © 2022 IEEE.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

Development of A Semi Automatic-Robotic Arm for Swab Testing and Intimation System Using Robotic Process Automation

The SARS-CoV-2 pandemic is a health catastrophe and its consequences are severe and far-reaching. Globally the rapid community transmission is being restrained through testing to effectively mitigate and hence suppress the spread. While this has been widely propagated, the healthcare workers are being exposed to the citizens who may have contracted the virus or while handling samples at test. The scarcity of PPE kits and other essentials have added to the crisis. The paper proposes a semi-automatic robotic arm that would minimize human interaction while collecting swab and storing it, which hinders the spread and exposure of health workers to the virus. The Robotic Arm performs the tasks currently performed by medical practitioners and also automates the process of report generation and intimation to the persons who underwent the test using Robotic Process Automation. © 2022 IEEE.

Administrative Liability for Vaccination with an Age-Inappropriate SARS-CoV-2 Vaccine: Latvian Experience

The duty of ensuring epidemiological safety, including the duty to ensure vaccination against SARS-CoV-2 to people, is included in the framework of the national constitutional rights. The healthcare institutions providing vaccination and medical practitioners performing vaccination are one of the key assets of the national health care system, to whom the duty in the field of public health and protection of lives that is a part of human rights have been delegated. Violation of the epidemiological safety requirements in the Republic of Latvia, if it may cause a risk to human health, is subject to a fine. In this study, the authors have analysed the administrative offence cases, in which administrative liability has been imposed on medical institutions for performing vaccination with age-inappropriate vaccine, explain separation of administrative liability from criminal liability in such cases, reveal compensation mechanisms in the event of consequences, when inappropriate vaccination has caused harm to persons’ life or health. The results of the research show that no appropriate security measures have been introduced in the medical institutions to prevent or avoid administrative offences in particular cases, as the result medical institutions were subject to first-time application of administrative liability. Besides, there are lack sufficiently secure system for the examination and registration of patients in the medical institutions. The minor patients were unsecured and have been vaccinated with an inappropriate vaccine, because a specific (non appropriate) vaccine has been requested by the minors’ parents or the minors themselves. [ FROM AUTHOR]

Transfer Learning based on Covid-19 Classification using CT Scan Image

Initially, the coronavirus infection has been diagnosed by using the Chest CT scan and x-ray images of the patients. An accurate representation of the victim's respiratory system allows the medical practitioners to detect the covid-19 infection. The first step of the proposed approach is to preprocess the image in order to eliminate any undesirable noise that may be present in medical images. Following that, the intended features are retrieved from a processed image. Finally, Transfer Learning is used to categorize the data. The CT scan based representations are separated by using a U-net simulation, and the split representation is then used to train and analyze the data by using the v3 simulator, which helps to differentiate the coronavirus infection and pneumonia infection and securely protect the resulting documents. © 2022 IEEE.

Driving Impact in Claims Denial Management Using Artificial Intelligence

A healthcare provider’s ability to quickly and efficiently process claims and quantify denial rates is critical to ensure smooth revenue cycle management and medical reimbursement. But the hospitals and medical practitioners are receiving more claim denials from payers, with the average rate of denial steadily increasing year over year. The recent COVID-19 pandemic has further accelerated the denial rate. An accurate denial detection algorithm can help to reduce the burden on healthcare providers. In this study, we propose a boosting-based machine learning framework to predict the likelihood of claims being denied along with the reason code at a line level. Prediction at a line level provides a finer-grained explanation to the administrative staff by pointing out the specific line for corrections. The list of important features provides an interpretable solution to the healthcare providers which enables them to create the right edits and correct the claim before going out to the payer. This in turn helps the healthcare provider dramatically improve both net patient revenue and cash flow. They can also put a check on their costs, as fewer denials mean less rework, resources, and time devoted to appealing and recovering denied claims. The denial model showed good performance with Area Under the Curve (AUC) of 0.80 and 0.82 for professional and institutional claims respectively. According to our estimates, the model has the potential to save 15%–50% of the denial cost for a healthcare provider. This in turn would have a tremendous impact on the healthcare costs as well as help make the healthcare process smoother. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Exploratory Analysis of the US COVID-19 Vaccine Distribution

The pandemic of Covid-19 was a huge challenge for people, the economies of companies and nations. The supply chain, which is a link from suppliers to customers, is one of the many sectors hugely affected by the Covid-19 pandemic. Many suppliers were forced to shut down operations due to the pandemic and the transportation industry suffered immensely. World leaders, medical practitioners and pharmaceutical companies began to talk about vaccine development to help in the fight against the pandemic. A breakthrough in the Covid-19 vaccine development brought smiles again to the world as many countries were already struggling to deal with the effect of this pandemic. Since the supply chain industry has been gravely impacted by the pandemic, there rises another challenge in the distribution of the developed Covid-19 Vaccine. Using Statistical Analysis System (SAS) and a combination of different multivariate methods, this research explores the United States Covid-19 and Vaccine distribution dataset to uncover trends affecting the Covid-19 Vaccine Supply Chain (VSC). Furthermore, this research provides some suggestions on how to improve the Covid-19 VSC using supply chain drivers such as facilities, transportations, and information. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Deep Pre-Trained Convolutional Neural System for High-Accuracy Covid19 Forecasting from Chest X-Rays

The continuous battle against the variants of Corona Virus demands speedy treatment and quick diagnostic reporting on priority basis. With millions of people contracting the infection every day and a mortality rate of 2%, our goal is to solve this growing problem by developing an important and substantive method for diagnosing COVID19 patients. Due to a proportionally reduced number of medical practitioners, testing kits, and other resources in densely populated nations, the exponential development of COVID19 cases is having a significant impact on the health care system, making it increasingly important to identify infected patients. The goal of this work is to develop an exact, productive and time-saving algorithm to identify positive corona patients that addresses the aforementioned issues. In this paper, a Deep Convolution Neural Network model called "EfficientNet"is implemented and explored that can reveal significant diagnostic characteristics to enable radiologists and medical specialists locate COVID-19 infected patients using X-ray pictures of the chest and aid in the fight against the pandemic. The experimental findings conclusively indicate that an accuracy rate of 99.71 percent was obtained for binary classification of Non-COVID and COVID Chest X-ray pictures. Our pretrained Deep Learning classification model can be a significant contribution to recognizing COVID-19 inflicted individuals due to its high diagnostic accuracy. © 2022 IEEE.

ANDTi Virtual Assistant

Due of the current COVID-19 pandemic crises, there is a worldwide need for quick medical findings. Furthermore, due to a lack of medical facilities and medical practitioners' hectic schedules, several examinations must now be performed by the general public. Also because of the high rate of transmissibility of COVID-19, even asymptomatic patients can readily transfer the virus to others, faster detection is critical during the initial phase of COVID-19, which is early identification. The earlier a patient is detected;the better the virus's spread may be stopped and the patient can undergo proper treatment. As the nationwide vaccination process is in its later part, it is obvious that the government will uplift its regulations and the employees will have to return to their workplaces or offices. As a solution to this upcoming urgency the authors would like to propose a solution to identify COVID-19 patients in advance at corporate level. As an IoT based solution a device is supposed to be setup on top of each employee's desk, which in return will be used to monitor the oxygen level, temperature, and heartbeat rate of the employees. © 2022 IEEE.

Mime 3D: A Patient Monitoring System

The Covid-19 Pandemic has affected the entire world. Most notably, the healthcare industry has been under constant pressure to treat patients. Spikes in the number of patients have put the workforce under tremendous pressure. Doctors and nurses are finding it difficult to observe multiple patients at the same time. In addition to that, medical practitioners are reluctant to deal with the diagnosis and treatments, as it requires frequent physical intervention. The aim of this project is to reduce this strain on medical practitioners by developing a system that aims to constantly track the activity of the patients and replicate the same using a 3D Human Model. For this multiple Inertial Motion Sensors (IMU's) are used that will collect the motion data of the joints of the patient, with help of which our 3D Model will replicate the actions. The system will use Internet of Things and Cloud Computing to collect and transfer data to the web application. All the activity of the patient can be monitored using fully authenticated web applications by doctors and even by the family members. Thus with the help of the technology patients can be monitored without any physical intervention and the risk of getting affected by viruses or diseases for the doctors is also minimized. © 2022 IEEE.

An Intelligent Robot Assisting Medical Practitioners to Aid Potential Covid-19 Patients

The purpose of this paper is to develop a robot-nurse capable of assisting human health professionals in a COVID-19 isolation unit such that health care professionals may discontinue wearing the personal protective kit that they previously wore when dealing with patients at isolation units. The robot can perform most of the tasks carried out by human nurses in a hospital. It can take a patient's temperature without touching them, administer medicines at the appropriate time, assist in sanitizing the individual and their hands, and offer a section for sterilizing medical equipment. [14] © 2021 IEEE.

A Federated Learning Approach to Pneumonia Detection

Pneumonia Detection has been a real problem for the last few centuries. Detecting Pneumonia has been a job for the skilled, such as doctors and medical practitioners. Visiting doctors in this time in many countries is very tough with Covid-19 on the rise and stricter lockdown regulations. Deep Learning has helped build many systems and algorithms over the years to detect pneumonia using X-ray images. Such Deep Learning models are first trained on many X-ray images that would be collected from multiple hospitals and diagnostic centers and then can be deployed centrally for people to use them. However, building such models is impeded by the problem of garnering mass data from hospitals due to data confidentiality between patients and hospitals. For that, we propose a system where detecting Pneumonia would be done using a Deep Learning model with a Federated Learning approach and achieve an accuracy of around 90%. This will build a central model by training local models in different hospitals with their own data, maintaining all patient data privacy. © 2021 IEEE.