Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Applying Machine Learning Techniques to Implementation Science

Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.

Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

INTRODUCTION

Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes.

METHODS

We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance.

RESULTS

Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators.

CONCLUSION

Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

A deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines

Throughout the pandemic era, COVID-19 was one of the remarkable unexpected situations over the past few years, but with the decentralization and globalization of efforts and knowledge, a successful vaccine-based control strategy was efficiently designed and applied worldwide. On the other hand, excused confusion and hesitation have widely impacted public health. This paper aims to reduce COVID-19 vaccine hesitancy taking into consideration the patient's medical history. The dataset used in this study is the Vaccine Adverse Event Reporting System (VAERS) dataset which was created as a corporation between the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to gather reported side effects that may be caused by PFIEZER, JANSSEN, and MODERNA vaccines. In this paper, a Deep Learning (DL) model has been developed to identify the relationship between a certain type of COVID-19 vaccine (i.e. PFIEZER, JANSSEN, and MODERNA) and the adverse reactions that may occur in vaccinated patients. The adverse reactions under study are the recovery condition, possibility to be hospitalized, and death status. In the first phase of the proposed model, the dataset has been pre-proceesed, while in the second phase, the Pigeon swarm optimization algorithm is used to optimally select the most promising features that affect the performance of the proposed model. The patient's status after vaccination dataset is grouped into three target classes (Death, Hospitalized, and Recovered). In the third phase, Recurrent Neural Network (RNN) is implemented for both each vaccine type and each target class. The results show that the proposed model gives the highest accuracy scores which are 96.031% for the Death target class in the case of PFIEZER vaccination. While in JANSSEN vaccination, the Hospitalized target class has shown the highest performance with an accuracy of 94.7%. Finally, the model has the best performance for the Recovered target class in MODERNA vaccination with an accuracy of 97.794%. Based on the accuracy and the Wilcoxon Signed Rank test, we can conclude that the proposed model is promising for identifying the relationship between the side effects of COVID-19 vaccines and the patient's status after vaccination. The study displayed that certain side effects were increased in patients according to the type of COVID-19 vaccines. Side effects related to CNS and hemopoietic systems demonstrated high values in all studied COVID-19 vaccines. In the frame of precision medicine, these findings can support the medical staff to select the best COVID-19 vaccine based on the medical history of the patient.

A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.

Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms

BACKGROUND

Polypharmacy (PP) is increasingly common in Iran, and contributes to the substantial burden of drug-related morbidity, increasing the potential for drug interactions and potentially inappropriate medications. Machine learning algorithms (ML) can be employed as an alternative solution for the prediction of PP. Therefore, our study aimed to compare several ML algorithms to predict the PP using the health insurance claims data and choose the best-performing algorithm as a predictive tool for decision-making.

METHODS

This population-based cross-sectional study was performed between April 2021 and March 2022. After feature selection, information about 550 thousand patients were obtained from National Center for Health Insurance Research (NCHIR). Afterwards, several ML algorithms were trained to predict PP. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated.

RESULTS

The study sample comprised 554 133 adults with a median (IQR) age of 51 years (40 - 62) that nested in 27 cities within the Khuzestan province of Iran. Most of the patients were female (62.5%), married (63.5%), and employed (83.2%) during the last year. The prevalence of PP in all populations was about 36.0%. After performing the feature selection, out of 23 features, the number of prescriptions, Insurance coverage for prescription drugs, and hypertension were found as the top three predictors. Experimental results showed that Random Forest (RF) performed better than other ML algorithms with recall, specificity, accuracy, precision and F1-score of 63.92%, 89.92%, 79.99%, 63.92% and 63.92% respectively.

CONCLUSION

It was found that ML provides a reasonable level of accuracy in predicting polypharmacy. Therefore, the prediction models based on ML, especially the RF algorithm, performed better than other methods for predicting PP in Iranian people in terms of the performance criteria.

Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives

In light of the constantly changing terrain of the COVID outbreak, medical specialists have implemented proactive schemes for vaccine production. Despite the remarkable COVID-19 vaccine development, the virus has mutated into new variants, including delta and omicron. Currently, the situation is critical in many parts of the world, and precautions are being taken to stop the virus from spreading and mutating. Early identification and diagnosis of COVID-19 are the main challenges faced by emerging technologies during the outbreak. In these circumstances, emerging technologies to tackle Coronavirus have proven magnificent. Artificial intelligence (AI), big data, the internet of medical things (IoMT), robotics, blockchain technology, telemedicine, smart applications, and additive manufacturing are suspicious for detecting, classifying, monitoring, and locating COVID-19. Henceforth, this research aims to glance at these COVID-19 defeating technologies by focusing on their strengths and limitations. A CiteSpace-based bibliometric analysis of the emerging technology was established. The most impactful keywords and the ongoing research frontiers were compiled. Emerging technologies were unstable due to data inconsistency, redundant and noisy datasets, and the inability to aggregate the data due to disparate data formats. Moreover, the privacy and confidentiality of patient medical records are not guaranteed. Hence, Significant data analysis is required to develop an intelligent computational model for effective and quick clinical diagnosis of COVID-19. Remarkably, this article outlines how emerging technology has been used to counteract the virus disaster and offers ongoing research frontiers, directing readers to concentrate on the real challenges and thus facilitating additional explorations to amplify emerging technologies.

Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach

BACKGROUND

The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage.

METHODS

A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model.

RESULTS

An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset.

CONCLUSIONS

The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

Superior possibilities and upcoming horizons for nanoscience in COVID-19: noteworthy approach for effective diagnostics and management of SARS-CoV-2 outbreak

The outbreak of COVID-19 has caused great havoc and affected many parts of the world. It has imposed a great challenge to the medical and health fraternity with its ability to continue mutating and increasing the transmission rate. Some challenges include the availability of current knowledge of active drugs against the virus, mode of delivery of the medicaments, its diagnosis, which are relatively limited and do not suffice for further prognosis. One recently developed drug delivery system called nanoparticles is currently being utilized in combating COVID-19. This article highlights the existing methods for diagnosis of COVID-19 such as computed tomography scan, reverse transcription-polymerase chain reaction, nucleic acid sequencing, immunoassay, point-of-care test, detection from breath, nanotechnology-based bio-sensors, viral antigen detection, microfluidic device, magnetic nanosensor, magnetic resonance platform and internet-of-things biosensors. The latest detection strategy based on nanotechnology, biosensor, is said to produce satisfactory results in recognizing SARS-CoV-2 virus. It also highlights the successes in the research and development of COVID-19 treatments and vaccines that are already in use. In addition, there are a number of nanovaccines and nanomedicines currently in clinical trials that have the potential to target COVID-19.

Hemogram-based decision tree models for discriminating COVID-19 from RSV in infants

OBJECTIVE

Decision trees are efficient and reliable decision-making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID-19) and respiratory syncytial virus (RSV) infection in infants.

METHODS

A cross-sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS-CoV-2) infection and 44 infants with RSV infection. In total, 23 hemogram-based instances were used to construct the decision tree models via 10-fold cross-validation method.

RESULTS

The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one.

CONCLUSION

Random forest and optimized forest models might have significant clinical applications, helping to speed up decision-making when SARS-CoV-2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing.

An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.

Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model

SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.

A survey of machine learning-based methods for COVID-19 medical image analysis

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.

Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System

OBJECTIVES

Gastroesophageal reflux disease (GERD) is a complex disease with a high worldwide prevalence. The Los Angeles classification (LA-grade) system is meaningful for assessing the endoscopic severity of GERD. Deep learning (DL) methods have been widely used in the field of endoscopy. However, few DL-assisted researches have concentrated on the diagnosis of GERD. This study is the first to develop a five-category classification DL model based on the LA-grade using explainable artificial intelligence (XAI).

MATERIALS AND METHODS

A total of 2081 endoscopic images were used for the development of a DL model, and the classification accuracy of the models and endoscopists with different levels of experience was compared.

RESULTS

Some mainstream DL models were utilized, of which DenseNet-121 outperformed. The area under the curve (AUC) of the DenseNet-121 was 0.968, and its classification accuracy (86.7%) was significantly higher than that of junior (71.5%) and experienced (77.4%) endoscopists. An XAI evaluation was also performed to explore the perception consistency between the DL model and endoscopists, which showed meaningful results for real-world applications.

CONCLUSIONS

The DL model showed a potential in improving the accuracy of endoscopists in LA-grading of GERD, and it has noticeable clinical application prospects and is worthy of further promotion.

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust

Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.

A residual network-based framework for COVID-19 detection from CXR images

In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a large population. Polymerase chain reaction is currently being utilized to diagnose COVID-19 in suspected patients; however, its sensitivity is quite low. The researchers also developed automated approaches for reliably and timely identifying COVID-19 from X-ray images. However, traditional machine learning-based image classification algorithms necessitate manual image segmentation and feature extraction, which is a time-consuming task. Due to promising results and robust performance, Convolutional Neural Network (CNN)-based techniques are being used widely to classify COVID-19 from Chest X-rays (CXR). This study explores CNN-based COVID-19 classification methods. A series of experiments aimed at COVID-19 detection and classification validates the viability of our proposed framework. Initially, the dataset is preprocessed and then fed into two Residual Network (ResNet) architectures for deep feature extraction, such as ResNet18 and ResNet50, whereas support vector machines with its multiple kernels, including Quadratic, Linear, Gaussian and Cubic, are used to classify these features. The experimental results suggest that the proposed framework efficiently detects COVID-19 from CXR images. The proposed framework obtained the best accuracy of 97.3% using ResNet50.

Multi-objective data enhancement for deep learning-based ultrasound analysis

Recently, Deep Learning based automatic generation of treatment recommendation has been attracting much attention. However, medical datasets are usually small, which may lead to over-fitting and inferior performances of deep learning models. In this paper, we propose multi-objective data enhancement method to indirectly scale up the medical data to avoid over-fitting and generate high quantity treatment recommendations. Specifically, we define a main and several auxiliary tasks on the same dataset and train a specific model for each of these tasks to learn different aspects of knowledge in limited data scale. Meanwhile, a Soft Parameter Sharing method is exploited to share learned knowledge among models. By sharing the knowledge learned by auxiliary tasks to the main task, the proposed method can take different semantic distributions into account during the training process of the main task. We collected an ultrasound dataset of thyroid nodules that contains Findings, Impressions and Treatment Recommendations labeled by professional doctors. We conducted various experiments on the dataset to validate the proposed method and justified its better performance than existing methods.

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.

An Entropy-Based Measure of Complexity: An Application in Lung-Damage

The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.

DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia

A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients’ data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients’ data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients’ data.

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).

Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts

This study was designed to develop machine-learning models to predict COVID-19 mortality and identify its key features based on clinical characteristics and laboratory tests. For this, deep-learning (DL) and machine-learning (ML) models were developed using receiver operating characteristic (ROC) area under the curve (AUC) and F1 score optimization of 87 parameters. Of the two, the DL model exhibited better performance (AUC 0.8721, accuracy 0.84, and F1 score 0.76). However, we also blended DL with ML, and the ensemble model performed the best (AUC 0.8811, accuracy 0.85, and F1 score 0.77). The DL model is generally unable to extract feature importance; however, we succeeded by using the Shapley Additive exPlanations method for each model. This study demonstrated both the applicability of DL and ML models for classifying COVID-19 mortality using hospital-structured data and that the ensemble model had the best predictive ability.

Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System

Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.

A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort

Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recovery as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10 ~ 15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contain calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification

Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal sensy of 0.9422, specy of 0.9466, precn of 0.9494, accy of 0.9429, and F1score of 0.9394.

Knowledge graph analysis and visualization of AI technology applied in COVID-19

With the global outbreak of coronavirus disease (COVID-19) all over the world, artificial intelligence (AI) technology is widely used in COVID-19 and has become a hot topic. In recent 2 years, the application of AI technology in COVID-19 has developed rapidly, and more than 100 relevant papers are published every month. In this paper, we combined with the bibliometric and visual knowledge map analysis, used the WOS database as the sample data source, and applied VOSviewer and CiteSpace analysis tools to carry out multi-dimensional statistical analysis and visual analysis about 1903 pieces of literature of recent 2 years (by the end of July this year). The data is analyzed by several terms with the main annual article and citation count, major publication sources, institutions and countries, their contribution and collaboration, etc. Since last year, the research on the COVID-19 has sharply increased; especially the corresponding research fields combined with the AI technology are expanding, such as medicine, management, economics, and informatics. The China and USA are the most prolific countries in AI applied in COVID-19, which have made a significant contribution to AI applied in COVID-19, as the high-level international collaboration of countries and institutions is increasing and more impactful. Moreover, we widely studied the issues: detection, surveillance, risk prediction, therapeutic research, virus modeling, and analysis of COVID-19. Finally, we put forward perspective challenges and limits to the application of AI in the COVID-19 for researchers and practitioners to facilitate future research on AI applied in COVID-19.

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests

Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests

Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.

COVID-19 Detection Systems Using Deep-Learning Algorithms Based on Speech and Image Data

The global epidemic caused by COVID-19 has had a severe impact on the health of human beings. The virus has wreaked havoc throughout the world since its declaration as a worldwide pandemic and has affected an expanding number of nations in numerous countries around the world. Recently, a substantial amount of work has been done by doctors, scientists, and many others working on the frontlines to battle the effects of the spreading virus. The integration of artificial intelligence, specifically deep- and machine-learning applications, in the health sector has contributed substantially to the fight against COVID-19 by providing a modern innovative approach for detecting, diagnosing, treating, and preventing the virus. In this proposed work, we focus mainly on the role of the speech signal and/or image processing in detecting the presence of COVID-19. Three types of experiments have been conducted, utilizing speech-based, image-based, and speech and image-based models. Long short-term memory (LSTM) has been utilized for the speech classification of the patient’s cough, voice, and breathing, obtaining an accuracy that exceeds 98%. Moreover, CNN models VGG16, VGG19, Densnet201, ResNet50, Inceptionv3, InceptionResNetV2, and Xception have been benchmarked for the classification of chest X-ray images. The VGG16 model outperforms all other CNN models, achieving an accuracy of 85.25% without fine-tuning and 89.64% after performing fine-tuning techniques. Furthermore, the speech–image-based model has been evaluated using the same seven models, attaining an accuracy of 82.22% by the InceptionResNetV2 model. Accordingly, it is inessential for the combined speech–image-based model to be employed for diagnosis purposes since the speech-based and image-based models have each shown higher terms of accuracy than the combined model.

Applications of game theory in deep learning: a survey

This paper provides a comprehensive overview of the applications of game theory in deep learning. Today, deep learning is a fast-evolving area for research in the domain of artificial intelligence. Alternatively, game theory has been showing its multi-dimensional applications in the last few decades. The application of game theory to deep learning includes another dimension in research. Game theory helps to model or solve various deep learning-based problems. Existing research contributions demonstrate that game theory is a potential approach to improve results in deep learning models. The design of deep learning models often involves a game-theoretic approach. Most of the classification problems which popularly employ a deep learning approach can be seen as a Stackelberg game. Generative Adversarial Network (GAN) is a deep learning architecture that has gained popularity in solving complex computer vision problems. GANs have their roots in game theory. The training of the generators and discriminators in GANs is essentially a two-player zero-sum game that allows the model to learn complex functions. This paper will give researchers an extensive account of significant contributions which have taken place in deep learning using game-theoretic concepts thus, giving a clear insight, challenges, and future directions. The current study also details various real-time applications of existing literature, valuable datasets in the field, and the popularity of this research area in recent years of publications and citations.

Face mask recognition from audio: The MASC database and an overview on the mask challenge

The sudden outbreak of COVID-19 has resulted in tough challenges for the field of biometrics due to its spread via physical contact, and the regulations of wearing face masks. Given these constraints, voice biometrics can offer a suitable contact-less biometric solution; they can benefit from models that classify whether a speaker is wearing a mask or not. This article reviews the Mask Sub-Challenge (MSC) of the INTERSPEECH 2020 COMputational PARalinguistics challengE (ComParE), which focused on the following classification task: Given an audio chunk of a speaker, classify whether the speaker is wearing a mask or not. First, we report the collection of the Mask Augsburg Speech Corpus (MASC) and the baseline approaches used to solve the problem, achieving a performance of 71.8 % Unweighted Average Recall (UAR). We then summarise the methodologies explored in the submitted and accepted papers that mainly used two common patterns: (i) phonetic-based audio features, or (ii) spectrogram representations of audio combined with Convolutional Neural Networks (CNNs) typically used in image processing. Most approaches enhance their models by adapting ensembles of different models and attempting to increase the size of the training data using various techniques. We review and discuss the results of the participants of this sub-challenge, where the winner scored a UAR of 80.1 % . Moreover, we present the results of fusing the approaches, leading to a UAR of 82.6 % . Finally, we present a smartphone app that can be used as a proof of concept demonstration to detect in real-time whether users are wearing a face mask; we also benchmark the run-time of the best models.

COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds

In the last 2 years, medical researchers and clinical scientists have paid close attention to the problem of respiratory sound classification to classify COVID-19 disease symptoms. In the physical world, very few AI-based (Artificial Intelligence) techniques are often used to detect COVID-19/SARS-CoV-2 respiratory disease symptoms from the human respiratory system-generated acoustic sounds such as acoustic voice sound, breathing (inhale and exhale) sounds, and cough sound. We propose a light-weight Convolutional Neural Network (CNN) with Modified-Mel-frequency Cepstral Coefficient (M-MFCC) using different depths and kernel sizes to classify COVID-19 and other respiratory sound disease symptoms such as Asthma, Pertussis, and Bronchitis. The proposed network outperforms conventional feature extraction models and existing Deep Learning (DL) models for COVID-19/SARS-CoV-2 classification accuracy in the range of 4-10%. The model's performance is compared with the COVID-19 crowdsourced benchmark dataset and gives a competitive performance. We applied different receptive fields and depths in the proposed model to get different contextual information that should aid in classification. And our experiments suggested 1 × 12 receptive fields and a depth of 5-Layer for the light-weight CNN to extract and identify the features from respiratory sound data. The model is also trained and tested with different modalities of data to showcase its effectiveness in classification.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients

During 2020 and 2021, managing limited healthcare resources and hospital beds has been a fundamental aspect of the fight against the COVID-19 pandemic. Predicting in advance the length of stay, and in particular identifying whether a patient is going to stay in the hospital longer or less than a week, can provide important support in handling resources allocation. However, there have been significant changes in terms of containment measures, virus diffusion, new treatments, vaccines, and new variants of SARS-CoV-2 during the last period. These changes pose several conceptual drift issues that can limit the usefulness of machine learning in this context. In this work, we present a machine learning system trained and tested using data from more than 6000 hospitalised patients in northern Italy, distributed over almost two years of pandemic. We show how machine learning can be effective even by analysing data over this long period of time, also exploiting a model that predicts the patient's outcome in terms of discharge or death. Furthermore, learning from data that also consider deceased patients is a common issue in predicting the length of stay because they have severe conditions similar to patients with a long stay period, but may actually have a very short duration of hospitalisation. For this purpose, we present a method for handling data from alive and deceased patients, exploiting more patient records, increasing the robustness of the model and its performance in this task. Finally, we investigate the features that are most relevant to the prediction of the simplified length of stay.

Cough/X-ray/CT (CXC) website for testing COVID-19 and auto-informing results

Despite the development of vaccines and the emergence of various treatments for COVID-19, the number of confirmed cases of the coronavirus disease (COVID-19) is increasing worldwide, and it is unlikely that the disease will ever disappear completely. Having a non-contact remote testing system can improve the workload of health-care centers and contribute to reducing the infection by recommending early self-isolation for those who suffer from a cough. In the proposed system, patients can upload an audio cough recording via mobile phones through the suggested Cough/X-ray/CT website and then receive the diagnosis within seconds on the same phone. Moreover, in the case of infection, the health center and the community are informed in addition to automatically calling the mobile phones of the injured cases. The higher proposed accuracy with deep cough training was achieved on the ResNet152v2 model after converting the cough signal into an image using the Mel-spectrogram, where the accuracy was 99.95%, the sensitivity was 100%, and the specificity was 99%.

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as ‘machine learning’, blockchain, ‘Internet of Things or IoT’, and keywords conjoined with ‘healthcare’ and ‘health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system (n = 6) or explored the function of an intrinsic AI system in a CAD software (n = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images

The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

An ensemble learning approach to digital corona virus preliminary screening from cough sounds

This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.