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Halwani MA, Halwani MA. Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence. Healthcare (Basel) 2024; 12:1694. [PMID: 39273719 PMCID: PMC11395195 DOI: 10.3390/healthcare12171694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 08/19/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
BACKGROUND COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. METHODS A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. RESULTS The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. CONCLUSIONS Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms.
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Affiliation(s)
| | - Manal Ahmed Halwani
- Emergency Department, College of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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2
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Dong L, Zhang Y, Fu B, Swart C, Jiang H, Liu Y, Huggett J, Wielgosz R, Niu C, Li Q, Zhang Y, Park SR, Sui Z, Yu L, Liu Y, Xie Q, Zhang H, Yang Y, Dai X, Shi L, Yin Y, Fang X. Reliable biological and multi-omics research through biometrology. Anal Bioanal Chem 2024; 416:3645-3663. [PMID: 38507042 DOI: 10.1007/s00216-024-05239-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024]
Abstract
Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.
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Affiliation(s)
- Lianhua Dong
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
| | - Yu Zhang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Boqiang Fu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Claudia Swart
- Physikalisch-Technische Bundesanstalt, 38116, Braunschweig, Germany
| | | | - Yahui Liu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Jim Huggett
- National Measurement Laboratory at LGC (NML), Teddington, Middlesex, UK
| | - Robert Wielgosz
- Bureau International Des Poids Et Mesures (BIPM), Pavillon de Breteuil, 92312, Sèvres Cedex, France
| | - Chunyan Niu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Qianyi Li
- BGI, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yongzhuo Zhang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Sang-Ryoul Park
- Korea Research Institute of Standards and Science, Daejeon, Republic of Korea
| | - Zhiwei Sui
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | - Lianchao Yu
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China
| | | | - Qing Xie
- BGI, BGI-Shenzhen, Shenzhen, 518083, China
| | - Hongfu Zhang
- BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Xinhua Dai
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Ye Yin
- BGI, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Xiang Fang
- Center for Advanced Measurement of Science, National Institute of Metrology, Beijing, 100029, China.
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3
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Ahn H, Lee H. Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:6150-6166. [PMID: 38872573 DOI: 10.3934/mbe.2024270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control. This study aims to predict the transmission patterns of COVID-19 using machine learning, such as support vector machine, random forest, and XGBoost, using confirmed cases, death cases, and imported cases, respectively. The study categorizes the transmission trends into the three groups: L0 (decrease), L1 (maintain), and L2 (increase). We develop the risk index function to quantify changes in the transmission trends, which is applied to the classification of machine learning. A high accuracy is achieved when estimating the transmission trends for the confirmed cases (91.5-95.5%), death cases (85.6-91.8%), and imported cases (77.7-89.4%). Notably, the confirmed cases exhibit a higher level of accuracy compared to the data on the deaths and imported cases. L2 predictions outperformed L0 and L1 in all cases. Predicting L2 is important because it can lead to new outbreaks. Thus, this robust L2 prediction is crucial for the timely implementation of control policies for the management of transmission dynamics.
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Affiliation(s)
- Hyeonjeong Ahn
- Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea
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4
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Chauhan S, Edla DR, Boddu V, Rao MJ, Cheruku R, Nayak SR, Martha S, Lavanya K, Nigat TD. Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images. BMC Med Imaging 2024; 24:1. [PMID: 38166813 PMCID: PMC10759384 DOI: 10.1186/s12880-023-01155-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024] Open
Abstract
Deep learning is a highly significant technology in clinical treatment and diagnostics nowadays. Convolutional Neural Network (CNN) is a new idea in deep learning that is being used in the area of computer vision. The COVID-19 detection is the subject of our medical study. Researchers attempted to increase the detection accuracy but at the cost of high model complexity. In this paper, we desire to achieve better accuracy with little training space and time so that this model easily deployed in edge devices. In this paper, a new CNN design is proposed that has three stages: pre-processing, which removes the black padding on the side initially; convolution, which employs filter banks; and feature extraction, which makes use of deep convolutional layers with skip connections. In order to train the model, chest X-ray images are partitioned into three sets: learning(0.7), validation(0.1), and testing(0.2). The models are then evaluated using the test and training data. The LMNet, CoroNet, CVDNet, and Deep GRU-CNN models are the other four models used in the same experiment. The propose model achieved 99.47% & 98.91% accuracy on training and testing respectively. Additionally, it achieved 97.54%, 98.19%, 99.49%, and 97.86% scores for precision, recall, specificity, and f1-score respectively. The proposed model obtained nearly equivalent accuracy and other similar metrics when compared with other models but greatly reduced the model complexity. Moreover, it is found that proposed model is less prone to over fitting as compared to other models.
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Affiliation(s)
- Sohamkumar Chauhan
- Department of Computer Science and Engineering, National Institute of Technology Goa, Ponda, 403401, Goa, India
| | - Damoder Reddy Edla
- Department of Computer Science and Engineering, National Institute of Technology Goa, Ponda, 403401, Goa, India
| | - Vijayasree Boddu
- Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Hanamkonda, 506004, Telangana, India
| | - M Jayanthi Rao
- Department of CSE, Aditya Institute of Technology and Management, Kotturu, Tekkali, Andhra Pradesh, India
| | - Ramalingaswamy Cheruku
- Department of Computer Science and Engineering, National Institute of Technology Warangal, Hanamkonda, 506004, Telangana, India
| | - Soumya Ranjan Nayak
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, Odisha, India
| | - Sheshikala Martha
- School of Computer Science and Artificial Intelligence, SR University, Warangal, 506004, Telangana, India
| | - Kamppa Lavanya
- University College of Sciences, Acharya Nagarjuna Univesity, Guntur, Andhra Pradesh, India
| | - Tsedenya Debebe Nigat
- Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma, Oromia, Ethiopia.
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5
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Sharan M, Vijay D, Yadav JP, Bedi JS, Dhaka P. Surveillance and response strategies for zoonotic diseases: a comprehensive review. SCIENCE IN ONE HEALTH 2023; 2:100050. [PMID: 39077041 PMCID: PMC11262259 DOI: 10.1016/j.soh.2023.100050] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 10/29/2023] [Indexed: 07/31/2024]
Abstract
Out of all emerging infectious diseases, approximately 75% are of zoonotic origin, with their source often traced back to animals. The emergence of zoonoses is driven by a complex interplay between anthropogenic, genetic, ecological, socioeconomic, and climatic factors. This intricate web of influences poses significant challenges for the prediction and prevention of zoonotic outbreaks. Effective coordination and collaboration among the animal, human, and environmental health sectors are essential for proactively addressing major zoonotic diseases. Despite advancements in surveillance and diagnostic practices, the emergence of zoonoses continues to be a pressing global concern. Therefore, prioritizing zoonotic disease surveillance is of paramount importance as part of a comprehensive disease prevention and containment strategy. Furthermore, evaluating existing surveillance systems provides insights into the challenges faced, which can be mitigated through implementation of One Health principles involving relevant stakeholders. To initiate multisectoral partnerships, it is crucial to identify the priorities and core themes of surveillance systems with equitable inputs from various sectors. Strengthening surveillance, promoting data sharing, enhancing laboratory testing capabilities, and fostering joint outbreak responses in both the human and animal health sectors will establish the necessary infrastructure to effectively prevent, predict, detect, and respond to emerging health threats, thereby reinforcing global health security. This review assesses existing surveillance approaches by offering an overview of global agencies engaged in monitoring zoonoses and outlines the essential components required at the human-animal-environment interface for designing comprehensive surveillance networks. Additionally, it discusses the key steps necessary for executing effective zoonotic disease surveillance through a One Health approach, while highlighting the key challenges encountered in establishing such a robust surveillance system.
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Affiliation(s)
- Manjeet Sharan
- Animal and Fisheries Resources Department, Patna, Bihar, 800015, India
| | - Deepthi Vijay
- Department of Veterinary Public Health, College of Veterinary and Animal Sciences, Kerala Veterinary and Animal Sciences University, Mannuthy, Thrissur, 680651, India
| | - Jay Prakash Yadav
- Department of Veterinary Public Health and Epidemiology, College of Veterinary Science, Guru Angad Dev Veterinary and Animal Sciences University, Rampura Phul, Bathinda, 151103, India
| | - Jasbir Singh Bedi
- Centre for One Health, College of Veterinary Science, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, 141004, India
| | - Pankaj Dhaka
- Centre for One Health, College of Veterinary Science, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, 141004, India
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6
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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7
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Young D, Houshmand B, Tan CC, Kirubarajan A, Parbhakar A, Dada J, Whittle W, Sobel ML, Gomez LM, Rüdiger M, Pecks U, Oppelt P, Ray JG, Hobson SR, Snelgrove JW, D'Souza R, Kashef R, Sussman D. Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study. BMC Pregnancy Childbirth 2023; 23:553. [PMID: 37532986 PMCID: PMC10394879 DOI: 10.1186/s12884-023-05679-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 05/04/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. METHODS An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. RESULTS The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. CONCLUSIONS We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19.
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Affiliation(s)
- Dylan Young
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria St, Toronto, ON, M5B 0A1, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University & St. Michael's Hospital, Toronto, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Canada
| | - Bita Houshmand
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria St, Toronto, ON, M5B 0A1, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University & St. Michael's Hospital, Toronto, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Canada
| | - Chunyi Christie Tan
- MD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Abirami Kirubarajan
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Canada
| | - Ashna Parbhakar
- MD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jazleen Dada
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Wendy Whittle
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto, Canada
| | - Mara L Sobel
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto, Canada
| | - Luis M Gomez
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, INOVA Health System, Falls Church, VA, USA
| | - Mario Rüdiger
- Saxony Center for Feto-Neonatal Health, Medizinische Fakultät Der TU Dresden, Dresden, Germany
| | - Ulrich Pecks
- Department of Obstetrics and Gynaecology, University Hospital of Schleswig-Holstein, Kiel, Germany
| | - Peter Oppelt
- Department for Gynecology, Obstetrics and Gynecological Endocrinology, Kepler University Hospital Linz, Johannes Kepler Universität Linz, Altenberger Strasse 69, 4040, Linz, Austria
| | - Joel G Ray
- MD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Obstetrics and Gynaecology, St, Michael's Hospital, Toronto, Canada
| | - Sebastian R Hobson
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto, Canada
| | - John W Snelgrove
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto, Canada
| | - Rohan D'Souza
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto, Canada
- Department of Obstetrics & Gynaecology and Health Research Methods Evidence and Impact, McMaster University, Hamilton, Canada
| | - Rasha Kashef
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria St, Toronto, ON, M5B 0A1, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University & St. Michael's Hospital, Toronto, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Canada
| | - Dafna Sussman
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria St, Toronto, ON, M5B 0A1, Canada.
- Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University & St. Michael's Hospital, Toronto, Canada.
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Canada.
- Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Canada.
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Dokeroglu T. A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients. PeerJ Comput Sci 2023; 9:e1430. [PMID: 37346714 PMCID: PMC10280461 DOI: 10.7717/peerj-cs.1430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/18/2023] [Indexed: 06/23/2023]
Abstract
Harris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.
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Affiliation(s)
- Tansel Dokeroglu
- Cankaya University, Software Engineering Department, Ankara, Turkey
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9
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Abdel-Hamid L. Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-23. [PMID: 37362648 PMCID: PMC10175919 DOI: 10.1007/s11042-023-15485-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/01/2022] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
Chest computer tomography (CT) provides a readily available and efficient tool for COVID-19 diagnosis. Wavelet and contourlet transforms have the advantages of being localized in both space and time. In addition, multiresolution analysis allows for the separation of relevant image information in the different subbands. In the present study, transform-based features were investigated for COVID-19 classification using chest CT images. Several textural and statistical features were computed from the approximation and detail subbands in order to fully capture disease symptoms in the chest CT images. Initially, multiresolution analysis was performed considering three different wavelet and contourlet levels to determine the transform and decomposition level most suitable for feature extraction. Analysis showed that contourlet features computed from the first decomposition level (L1) led to the most reliable COVID-19 classification results. The complete feature vector was computed in less than 25 ms for a single image having of resolution 256 × 256 pixels. Next, particle swarm optimization (PSO) was implemented to find the best set of L1-Contourlet features for enhanced performance. Accuracy, sensitivity, specificity, precision, and F-score of a 100% were achieved by the reduced feature set using the support vector machine (SVM) classifier. The presented contourlet-based COVID-19 detection method was also shown to outperform several state-of-the-art deep learning approaches from literature. The present study demonstrates the reliability of transform-based features for COVID-19 detection with the advantage of reduced computational complexity. Transform-based features are thus suitable for integration within real-time automatic screening systems used for the initial screening of COVID-19.
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Affiliation(s)
- Lamiaa Abdel-Hamid
- Department of Electronics & Communication, Faculty of Engineering, Misr International University (MIU), Cairo, Egypt
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10
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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11
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Ullah Z, Usman M, Gwak J. MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 216:119475. [PMID: 36619348 PMCID: PMC9810379 DOI: 10.1016/j.eswa.2022.119475] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/28/2022] [Accepted: 12/22/2022] [Indexed: 06/12/2023]
Abstract
Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea
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12
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Xu SM, Dong D, Li W, Bai T, Zhu MZ, Gu GS. Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements. World J Clin Cases 2023; 11:1477-1487. [PMID: 36926411 PMCID: PMC10011995 DOI: 10.12998/wjcc.v11.i7.1477] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Femoral trochlear dysplasia (FTD) is an important risk factor for patellar instability. Dejour classification is widely used at present and relies on standard lateral X-rays, which are not common in clinical work. Therefore, magnetic resonance imaging (MRI) has become the first choice for the diagnosis of FTD. However, manually measuring is tedious, time-consuming, and easily produces great variability.
AIM To use artificial intelligence (AI) to assist diagnosing FTD on MRI images and to evaluate its reliability.
METHODS We searched 464 knee MRI cases between January 2019 and December 2020, including FTD (n = 202) and normal trochlea (n = 252). This paper adopts the heatmap regression method to detect the key points network. For the final evaluation, several metrics (accuracy, sensitivity, specificity, etc.) were calculated.
RESULTS The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96. All values were superior to junior doctors and intermediate doctors, similar to senior doctors. However, diagnostic time was much lower than that of junior doctors and intermediate doctors.
CONCLUSION The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.
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Affiliation(s)
- Sheng-Ming Xu
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Dong Dong
- Department of Radiology, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Wei Li
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
| | - Tian Bai
- College of Computer Science and Technology, Jilin University, Changchun 130000, Jilin Province, China
| | - Ming-Zhu Zhu
- College of Computer Science and Technology, Jilin University, Changchun 130000, Jilin Province, China
| | - Gui-Shan Gu
- Department of Orthopedic Surgery, The First Hospital of Jilin University, Changchun 130000, Jilin Province, China
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13
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Najaran MHT. An evolutionary ensemble learning for diagnosing COVID-19 via cough signals. INTELLIGENT MEDICINE 2023; 3:S2667-1026(23)00002-5. [PMID: 36743333 PMCID: PMC9882956 DOI: 10.1016/j.imed.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/30/2023]
Abstract
Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose via cough signals the COVID-19 disease. Methods The proposed algorithm is an ensemble scheme that consists of a number of base learners, where each base learner uses a different feature extractor method, including statistical approaches and convolutional neural networks (CNN) for automatic feature extraction. Features are extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners are aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposes a memetic algorithm for training the CNNs in the base-learners, which combines the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms. Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion This research showed that COVID-19 could be diagnosed via cough signals and CNNs could be employed to process these signals and it may be further improved by the optimization of CNN architecture.
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14
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Kwekha-Rashid AS, Abduljabbar HN, Alhayani B. Coronavirus disease (COVID-19) cases analysis using machine-learning applications. APPLIED NANOSCIENCE 2023; 13:2013-2025. [PMID: 34036034 PMCID: PMC8138510 DOI: 10.1007/s13204-021-01868-7] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/04/2021] [Indexed: 12/23/2022]
Abstract
Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.
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Affiliation(s)
- Ameer Sardar Kwekha-Rashid
- Business Information Technology, College of Administration and Economics, University of Sulaimani, Sulaimaniya, Iraq
| | - Heamn N. Abduljabbar
- College of Education, Physics Department, Salahaddin University, Shaqlawa, Iraq ,Department of radiology and imagingFaculty of Medicine and Health Sciences, Universiti Putra Malaysia UPM, Seri Kembangan, Malaysia
| | - Bilal Alhayani
- Electronics and Communication Department, Yildiz Technical University, Istanbul, Turkey
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15
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Al-Khafaji HMR, Jaleel RA. Adopting effective hierarchal IoMTs computing with K-efficient clustering to control and forecast COVID-19 cases. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 104:108472. [PMID: 36408485 PMCID: PMC9647042 DOI: 10.1016/j.compeleceng.2022.108472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
The Internet of Medical Things (IoMTs) based on fog/cloud computing has been effectively proven to improve the controlling, monitoring, and care quality of Coronavirus disease 2019 (COVID-19) patients. One of the convenient approaches to assess symptomatic patients is to group patients with comparable symptoms and provide an overview of the required level of care to patients with similar conditions. Therefore, this study adopts an effective hierarchal IoMTs computing with K-Efficient clustering to control and forecast COVID-19 cases. The proposed system integrates the K-Means and K-Medoids clusterings to monitor the health status of patients, early detection of COVID-19 cases, and process data in real-time with ultra-low latency. In addition, the data analysis takes into account the primary requirements of the network to assist in understanding the nature of COVID-19. Based on the findings, the K-Efficient clustering with fog computing is a more effective approach to analyse the status of patients compared to that of K-Means and K-Medoids in terms of intra-class, inter-class, running time, the latency of network, and RAM consumption. In summary, the outcome of this study provides a novel approach for remote monitoring and handling of infected COVID-19 patients through real-time personalised treatment services.
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Affiliation(s)
| | - Refed Adnan Jaleel
- Information and Communication Engineering Department, Al-Nahrain University, Baghdad, Iraq
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16
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Cohen-McFarlane M, Xi P, Wallace B, Habashy K, Huq S, Goubran R, Knoefel F. Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:134-141. [PMID: 36578775 PMCID: PMC9788675 DOI: 10.1109/ojemb.2022.3202435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/06/2022] [Accepted: 08/25/2022] [Indexed: 12/31/2022] Open
Abstract
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.
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Affiliation(s)
- Madison Cohen-McFarlane
- AGE-WELL NCECarleton University Ottawa ON K1S 5B6 Canada
- AGE-WELL SAM3 National Innovation HubCarleton University Ottawa ON K1S 5B7 Canada
| | - Pengcheng Xi
- Digital Technologies Research CentreNational Research Council Canada Ottawa ON K1A 0R6 Canada
| | - Bruce Wallace
- AGE-WELL SAM3 National Innovation HubCarleton University Ottawa ON K1S 5B7 Canada
- AGE-WELL NCECarleton University Ottawa ON K1S 5B7 Canada
- Bruyère Research Institute Ottawa ON K1N 5C8 Canada
| | - Karim Habashy
- National Research Council Canada Ottawa ON K1A 0R6 Canada
| | - Saiful Huq
- Department of Systems and Computer Engineering, Carleton University Ottawa ON K1S 5B6 Canada
| | - Rafik Goubran
- AGE-WELL SAM3 National Innovation HubCarleton University Ottawa ON K1S 5B7 Canada
- Bruyère Research Institute Ottawa ON K1N 5C8 Canada
| | - Frank Knoefel
- Bruyère Research Institute, Bruyère Continuing CareElisabeth Bruyère Hospital Ottawa ON K1N 5C8 Canada
- AGE-WELL NCECarleton University Ottawa ON K1S 5B6 Canada
- AGE-WELL SAM3 National Innovation Hub Ottawa ON K1S 5B7 Canada
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17
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Adadi A, Lahmer M, Nasiri S. Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2022; 34:5898-5920. [PMID: 37520766 PMCID: PMC8831917 DOI: 10.1016/j.jksuci.2021.07.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/21/2021] [Accepted: 07/11/2021] [Indexed: 12/15/2022]
Abstract
Artificial Intelligence (AI) has played a substantial role in the response to the challenges posed by the current pandemic. The growing interest in using AI to handle Covid-19 issues has accelerated the pace of AI research and resulted in an exponential increase in articles and review studies within a very short period of time. Hence, it is becoming challenging to explore the large corpus of academic publications dedicated to the global health crisis. Even with the presence of systematic review studies, given their number and diversity, identifying trends and research avenues beyond the pandemic should be an arduous task. We conclude therefore that after the one-year mark of the declaration of Covid-19 as a pandemic, the accumulated scientific contribution lacks two fundamental aspects: Knowledge synthesis and Future projections. In contribution to fill this void, this paper is a (i) synthesis study and (ii) foresight exercise. The synthesis study aims to provide the scholars a consolidation of findings and a knowledge synthesis through a systematic review of the reviews (umbrella review) studying AI applications against Covid-19. Following the PRISMA guidelines, we systematically searched PubMed, Scopus, and other preprint sources from 1st December 2019 to 1st June 2021 for eligible reviews. The literature search and screening process resulted in 45 included reviews. Our findings reveal patterns, relationships, and trends in the AI research community response to the pandemic. We found that in the space of few months, the research objectives of the literature have developed rapidly from identifying potential AI applications to evaluating current uses of intelligent systems. Only few reviews have adopted the meta-analysis as a study design. Moreover, a clear dominance of the medical theme and the DNN methods has been observed in the reported AI applications. Based on its constructive systematic umbrella review, this work conducts a foresight exercise that tries to envision the post-Covid-19 research landscape of the AI field. We see seven key themes of research that may be an outcome of the present crisis and which advocate a more sustainable and responsible form of intelligent systems. We set accordingly a post-pandemic research agenda articulated around these seven drivers. The results of this study can be useful for the AI research community to obtain a holistic view of the current literature and to help prioritize research needs as we are heading toward the new normal.
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Affiliation(s)
- Amina Adadi
- ISIC Research Team of High School of Technology, LMMI Laboratory, Moulay Ismail University, Meknes, Morocco
| | - Mohammed Lahmer
- ISIC Research Team of High School of Technology, LMMI Laboratory, Moulay Ismail University, Meknes, Morocco
| | - Samia Nasiri
- ISIC Research Team of High School of Technology, LMMI Laboratory, Moulay Ismail University, Meknes, Morocco
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18
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Douglas MJ, Bell BW, Kinney A, Pungitore SA, Toner BP. Early COVID-19 respiratory risk stratification using machine learning. Trauma Surg Acute Care Open 2022; 7:e000892. [PMID: 36111138 PMCID: PMC9438026 DOI: 10.1136/tsaco-2022-000892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 07/26/2022] [Indexed: 12/15/2022] Open
Abstract
Background COVID-19 has strained healthcare systems globally. In this and future pandemics, providers with limited critical care experience must distinguish between moderately ill patients and those who will require aggressive care, particularly endotracheal intubation. We sought to develop a machine learning-informed Early COVID-19 Respiratory Risk Stratification (ECoRRS) score to assist in triage, by providing a prediction of intubation within the next 48 hours based on objective clinical parameters. Methods Electronic health record data from 3447 COVID-19 hospitalizations, 20.7% including intubation, were extracted. 80% of these records were used as the derivation cohort. The validation cohort consisted of 20% of the total 3447 records. Multiple randomizations of the training and testing split were used to calculate confidence intervals. Data were binned into 4-hour blocks and labeled as cases of intubation or no intubation within the specified time frame. A LASSO (least absolute shrinkage and selection operator) regression model was tuned for sensitivity and sparsity. Results Six highly predictive parameters were identified, the most significant being fraction of inspired oxygen. The model achieved an area under the receiver operating characteristic curve of 0.789 (95% CI 0.785 to 0.812). At 90% sensitivity, the negative predictive value was 0.997. Discussion The ECoRRS score enables non-specialists to identify patients with COVID-19 at risk of intubation within 48 hours with minimal undertriage and enables health systems to forecast new COVID-19 ventilator needs up to 48 hours in advance. Level of evidence IV.
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Affiliation(s)
- Molly J Douglas
- Department of Surgery, University of Arizona, Tucson, Arizona, USA
- Program in Applied Mathematics, University of Arizona, Tucson, Arizona, USA
| | - Brian W Bell
- Program in Applied Mathematics, University of Arizona, Tucson, Arizona, USA
| | - Adrienne Kinney
- Program in Applied Mathematics, University of Arizona, Tucson, Arizona, USA
| | - Sarah A Pungitore
- Program in Applied Mathematics, University of Arizona, Tucson, Arizona, USA
| | - Brian P Toner
- Program in Applied Mathematics, University of Arizona, Tucson, Arizona, USA
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19
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Di Benedetto M, Carrara F, Ciampi L, Falchi F, Gennaro C, Amato G. An embedded toolset for human activity monitoring in critical environments. EXPERT SYSTEMS WITH APPLICATIONS 2022; 199:117125. [PMID: 35431465 PMCID: PMC8990688 DOI: 10.1016/j.eswa.2022.117125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 01/27/2022] [Accepted: 03/28/2022] [Indexed: 05/13/2023]
Abstract
In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.
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Affiliation(s)
- Marco Di Benedetto
- Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR), Pisa, Italy
| | - Fabio Carrara
- Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR), Pisa, Italy
| | - Luca Ciampi
- Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR), Pisa, Italy
| | - Fabrizio Falchi
- Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR), Pisa, Italy
| | - Claudio Gennaro
- Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR), Pisa, Italy
| | - Giuseppe Amato
- Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR), Pisa, Italy
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20
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Comito C, Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artif Intell Med 2022; 128:102286. [PMID: 35534142 PMCID: PMC8958821 DOI: 10.1016/j.artmed.2022.102286] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023]
Abstract
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
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Affiliation(s)
- Carmela Comito
- National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Rende, Italy.
| | - Clara Pizzuti
- National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Rende, Italy.
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21
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Mohammedqasem R, Mohammedqasim H, Ata O. Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 100:107971. [PMID: 35399912 PMCID: PMC8985446 DOI: 10.1016/j.compeleceng.2022.107971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 05/03/2023]
Abstract
The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.
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Key Words
- ANN, Artificial Neural Network
- AUC, Area Under Curve
- CNN, Convolutional Neural Network
- COVID-19
- COVID-19, Coronavirus disease
- DL, Deep learning
- Imbalanced Dataset
- Internet of Things
- IoT, Internet of Things
- ML, Machine learning
- RFE, Recursive Feature Elimination
- RNN, Recurrent Neural Network
- Recursive feature elimination
- SMOTE, Synthetic Minority Oversampling Technique
- Synthetic minority oversampling technique
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Affiliation(s)
- Roa'a Mohammedqasem
- Department Of ECE, Institute Of Science, Altinbas University, Istanbul, Turkey
| | | | - Oguz Ata
- Department Of ECE, Institute Of Science, Altinbas University, Istanbul, Turkey
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22
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Speech as a Biomarker for COVID-19 Detection Using Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6093613. [PMID: 35444694 PMCID: PMC9014833 DOI: 10.1155/2022/6093613] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 11/30/2022]
Abstract
The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.
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23
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Anand S, Sharma V, Pourush R, Jaiswal S. A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19. Ann Med Surg (Lond) 2022; 76:103519. [PMID: 35401978 PMCID: PMC8975609 DOI: 10.1016/j.amsu.2022.103519] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/09/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022] Open
Abstract
The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used 'web of Science', 'Scopus' and 'goggle scholar' with the keywords of "SARS-CoV-2 detection" or "coronavirus 2019 detection" or "COVID 2019 detection" or "COVID 19 detection" "corona virus techniques for detection of COVID-19", "audio techniques for detection of COVID-19", "speech techniques for detection of COVID-19", for period of 2019-2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single "perfect" COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus.
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Affiliation(s)
- Satyajit Anand
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Vikrant Sharma
- Mechanical Engineering, Mody University of Science and Technology, India
| | - Rajeev Pourush
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Sandeep Jaiswal
- Biomedical Engineering, Mody University of Science and Technology, India
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A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data. Sci Rep 2022; 12:4329. [PMID: 35288579 PMCID: PMC8919158 DOI: 10.1038/s41598-022-07890-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 02/22/2022] [Indexed: 01/08/2023] Open
Abstract
AbstractCOVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.
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Lightweight Neural Network for COVID-19 Detection from Chest X-ray Images Implemented on an Embedded System. TECHNOLOGIES 2022. [DOI: 10.3390/technologies10020037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
At the end of 2019, a severe public health threat named coronavirus disease (COVID-19) spread rapidly worldwide. After two years, this coronavirus still spreads at a fast rate. Due to its rapid spread, the immediate and rapid diagnosis of COVID-19 is of utmost importance. In the global fight against this virus, chest X-rays are essential in evaluating infected patients. Thus, various technologies that enable rapid detection of COVID-19 can offer high detection accuracy to health professionals to make the right decisions. The latest emerging deep-learning (DL) technology enhances the power of medical imaging tools by providing high-performance classifiers in X-ray detection, and thus various researchers are trying to use it with limited success. Here, we propose a robust, lightweight network where excellent classification results can diagnose COVID-19 by evaluating chest X-rays. The experimental results showed that the modified architecture of the model we propose achieved very high classification performance in terms of accuracy, precision, recall, and f1-score for four classes (COVID-19, normal, viral pneumonia and lung opacity) of 21.165 chest X-ray images, and at the same time meeting real-time constraints, in a low-power embedded system. Finally, our work is the first to propose such an optimized model for a low-power embedded system with increased detection accuracy.
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Shah I, Doshi C, Patel M, Tanwar S, Hong WC, Sharma R. A Comprehensive Review of the Technological Solutions to Analyse the Effects of Pandemic Outbreak on Human Lives. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:311. [PMID: 35208634 PMCID: PMC8879197 DOI: 10.3390/medicina58020311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/12/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022]
Abstract
A coronavirus outbreak caused by a novel virus known as SARS-CoV-2 originated towards the latter half of 2019. COVID-19's abrupt emergence and unchecked global expansion highlight the inability of the current healthcare services to respond to public health emergencies promptly. This paper reviews the different aspects of human life comprehensively affected by COVID-19. It then discusses various tools and technologies from the leading domains and their integration into people's lives to overcome issues resulting from pandemics. This paper further focuses on providing a detailed review of existing and probable Artificial Intelligence (AI), Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), and Blockchain-based solutions. The COVID-19 pandemic brings several challenges from the viewpoint of the nation's healthcare, security, privacy, and economy. AI offers different predictive services and intelligent strategies for detecting coronavirus signs, promoting drug development, remote healthcare, classifying fake news detection, and security attacks. The incorporation of AI in the COVID-19 outbreak brings robust and reliable solutions to enhance the healthcare systems, increases user's life expectancy, and boosts the nation's economy. Furthermore, AR/VR helps in distance learning, factory automation, and setting up an environment of work from home. Blockchain helps in protecting consumer's privacy, and securing the medical supply chain operations. IoT is helpful in remote patient monitoring, distant sanitising via drones, managing social distancing (using IoT cameras), and many more in combating the pandemic. This study covers an up-to-date analysis on the use of blockchain technology, AI, AR/VR, and IoT for combating COVID-19 pandemic considering various applications. These technologies provide new emerging initiatives and use cases to deal with the COVID-19 pandemic. Finally, we discuss challenges and potential research paths that will promote further research into future pandemic outbreaks.
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Affiliation(s)
- Ishwa Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India; (I.S.); (C.D.); (M.P.)
| | - Chelsy Doshi
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India; (I.S.); (C.D.); (M.P.)
| | - Mohil Patel
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India; (I.S.); (C.D.); (M.P.)
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India; (I.S.); (C.D.); (M.P.)
| | - Wei-Chiang Hong
- Department of Information Management, Asia Eastern University of Science and Technology, New Taipei 22064, Taiwan
| | - Ravi Sharma
- Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, Uttarakhand, India;
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Khan IU, Aslam N, Anwar T, Alsaif HS, Chrouf SMB, Alzahrani NA, Alamoudi FA, Kamaleldin MMA, Awary KB. Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray. SENSORS 2022; 22:s22020669. [PMID: 35062629 PMCID: PMC8779361 DOI: 10.3390/s22020669] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 12/18/2022]
Abstract
The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.
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Affiliation(s)
- Irfan Ullah Khan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Nida Aslam
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
- Correspondence:
| | - Talha Anwar
- School of Computing, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan;
| | - Hind S. Alsaif
- Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (H.S.A.); (K.B.A.)
| | - Sara Mhd. Bachar Chrouf
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Norah A. Alzahrani
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh 12391, Saudi Arabia
| | - Fatimah Ahmed Alamoudi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Mariam Moataz Aly Kamaleldin
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Khaled Bassam Awary
- Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (H.S.A.); (K.B.A.)
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Peng Y, Liu E, Peng S, Chen Q, Li D, Lian D. Using artificial intelligence technology to fight COVID-19: a review. Artif Intell Rev 2022; 55:4941-4977. [PMID: 35002010 PMCID: PMC8720541 DOI: 10.1007/s10462-021-10106-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2021] [Indexed: 02/10/2023]
Abstract
In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.
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Affiliation(s)
- Yong Peng
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
| | - Enbin Liu
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
| | - Shanbi Peng
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500 China
| | - Qikun Chen
- School of Engineering, Cardiff University, Cardiff, CF24 3AA UK
| | - Dangjian Li
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
| | - Dianpeng Lian
- Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500 China
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Fuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during COVID-19. ENTROPY 2021; 24:e24010014. [PMID: 35052040 PMCID: PMC8774388 DOI: 10.3390/e24010014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 01/09/2023]
Abstract
The main research question concerned the identification of changes in the COVID-19 epidemiological situation using fuzzy clustering methods. This research used cross-sectional time series data obtained from the European Centre for Disease Prevention and Control. The identification of country types in terms of epidemiological risk was carried out using the fuzzy c-means clustering method. We also used the entropy index to measure the degree of fuzziness in the classification and evaluate the uncertainty of epidemiological states. The proposed approach allowed us to identify countries' epidemic states. Moreover, it also made it possible to determine the time of transition from one state to another, as well as to observe fluctuations during changes of state. Three COVID-19 epidemic states were identified in Europe, i.e., stabilisation, destabilisation, and expansion. The methodology is universal and can also be useful for other countries, as well as the research results being important for governments, politicians and other policy-makers working to mitigate the effects of the COVID-19 pandemic.
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Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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31
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Alonso AC, Silva-Santos PR, Quintana MSL, da Silva VC, Brech GC, Barbosa LG, Pompeu JE, Silva ECGE, da Silva EM, de Godoy CG, Greve JMD. Physical and pulmonary capacities of individuals with severe coronavirus disease after hospital discharge: A preliminary cross-sectional study based on cluster analysis. Clinics (Sao Paulo) 2021; 76:e3540. [PMID: 34852146 PMCID: PMC8595570 DOI: 10.6061/clinics/2021/e3540] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/29/2021] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE This study aimed to analyze the physical and pulmonary capacities of hospitalized patients with severe coronavirus disease and its correlation with the time of hospitalization and complications involved. METHODS A total of 54 patients, aged ≥18 years of both sexes, were evaluated 2-4 months after hospital discharge in São Paulo, Brazil. The physical characteristics analyzed were muscle strength, balance, flexibility, and pulmonary function. The K-means cluster algorithm was used to identify patients with similar physical and pulmonary capacities, related to the time of hospitalization. RESULTS Two clusters were derived using the K-means algorithm. Patients allocated in cluster 1 had fewer days of hospitalization, intensive care, and intubation than those in cluster 2, which reflected a better physical performance, strength, balance, and pulmonary condition, even 2-4 months after discharge. Days of hospitalization were inversely related to muscle strength, physical performance, and lung function: hand grip D (r=-0.28, p=0.04), Short Physical Performance Battery score (r=-0.28, p=0.03), and forced vital capacity (r=-0.29, p=0.03). CONCLUSION Patients with a longer hospitalization time and complications progressed with greater loss of physical and pulmonary capacities.
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Affiliation(s)
- Angelica Castilho Alonso
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
- Programa de Graduacao em Ciencias do Envelhecimento, Universidade Sao Judas Tadeu (USJT), Sao Paulo, SP, BR
| | - Paulo Roberto Silva-Santos
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Marília Simões Lopes Quintana
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Vanderlei Carneiro da Silva
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Guilherme Carlos Brech
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
- Programa de Graduacao em Ciencias do Envelhecimento, Universidade Sao Judas Tadeu (USJT), Sao Paulo, SP, BR
| | - Lorena Gonçalves Barbosa
- Programa de Graduacao em Ciencias do Envelhecimento, Universidade Sao Judas Tadeu (USJT), Sao Paulo, SP, BR
| | - José Eduardo Pompeu
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Erika Christina Gouveia e Silva
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Elizabeth Mendes da Silva
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Caroline Gil de Godoy
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Julia Maria D’Andréa Greve
- Laboratorio de Estudos do Movimento, Instituto de Ortopedia e Traumatologia (IOT), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
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Abstract
The coronavirus disease known today as COVID-19, has created tremendous chaos around the world, affecting people's lives and causing a large number of deaths. The WHO has accepted COVID-19 as a pandemic leading to a global health emergency. Global collaboration is sought in numerous quarters. Research efforts have been intensified all around the humankind. Most studies for COVID-19 are done based on statistical models which depend solely on correlation factors. The factor of causality has not been considered appropriately. The approach of Fuzzy Cognitive Maps (FCM) that is considering the causality factors is proposed, to investigate the whole spectrum of COVID-19. An FCM COVID-19 model is proposed having 10 symptoms-concepts. Early theoretical simulation studies using an FCM COVID-19 model and real data from the local hospital, have been conducted. Simulations with real patient data give excellent results. Future research directions are provided.
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Affiliation(s)
- Peter P Groumpos
- Emeritus Professor Department of Electrical and Computer Engineering, University of Patras,26500 Greece
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Arslan H. COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. COMPUTERS & INDUSTRIAL ENGINEERING 2021; 161:107666. [PMID: 34511707 PMCID: PMC8423779 DOI: 10.1016/j.cie.2021.107666] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/13/2021] [Accepted: 09/05/2021] [Indexed: 05/03/2023]
Abstract
This paper proposes an efficient and accurate method to predict coronavirus disease 19 (COVID-19) based on the genome similarity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a bat SARS-CoV-like coronavirus. We introduce similarity features to distinguish COVID-19 from other human coronaviruses by comparing human coronaviruses with a bat SARS-CoV-like coronavirus. In the proposed method each human coronavirus sequence is assigned to three similarity scores considering nucleotide similarities and mutations that lead to the strong absence of cytosine and guanine nucleotides. Next the proposed features are integrated with CpG island features of the genome sequences to improve COVID-19 prediction. Thus, each genome sequence is represented by five real numbers. We exhibit the effectiveness of the proposed features using six machine learning classifiers on a dataset including the genome sequences of human coronaviruses similar to SARS-CoV-2. The performances of the machine learning classifiers are close to each other and k-nearest neighbor classifier with similarity features achieves the best results with an accuracy of 99.2%. Moreover, k-nearest neighbor classifier with the integration of CpG based and similarity features has an admirable performance and achieves an accuracy of 99.8%. Experimental results demonstrate that similarity features remarkably decrease the number of false negatives and significantly improve the overall performance. The superiority of the proposed method is also highlighted by comparing with the state-of-the-art studies detecting COVID-19 from genome sequences.
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Affiliation(s)
- Hilal Arslan
- Department of Software Engineering, Ankara Yıldırım Beyazıt University, Turkey
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34
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A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting. FRACTAL AND FRACTIONAL 2021. [DOI: 10.3390/fractalfract5040175] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.
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Tsyganov V. Artificial intelligence, public control, and supply of a vital commodity like COVID-19 vaccine. AI & SOCIETY 2021; 38:1-10. [PMID: 34690448 PMCID: PMC8520116 DOI: 10.1007/s00146-021-01293-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/03/2021] [Indexed: 11/03/2022]
Abstract
The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system-a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico's actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico's control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico's control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico's control mechanism is considered on the case of supply of the COVID-19 vaccine in England.
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Affiliation(s)
- Vladimir Tsyganov
- Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, 117997 Russia
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Jordan E, Shin DE, Leekha S, Azarm S. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:130072-130093. [PMID: 35781925 PMCID: PMC8768956 DOI: 10.1109/access.2021.3113812] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/10/2021] [Indexed: 05/08/2023]
Abstract
This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.
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Affiliation(s)
- Elizabeth Jordan
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Delia E. Shin
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
| | - Surbhi Leekha
- Department of Epidemiology and Public HealthUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Shapour Azarm
- Department of Mechanical EngineeringUniversity of MarylandCollege ParkMD20742USA
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Yu C, Helwig EJ. Role of rehabilitation amidst the COVID-19 pandemic: a review. J Transl Med 2021; 19:376. [PMID: 34481486 PMCID: PMC8417619 DOI: 10.1186/s12967-021-03048-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 08/19/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 remains globally a highly infectious disease targeting multiple organs. Rehabilitation is increasingly valued among the supportive care fields to combat COVID-19 as currently definitive curative treatment remains largely absent. This narrative review is to address rehabilitation related topics associated with the treatment of COVID-19 patients. Nosocomial spread remains a high risk for healthcare workers, with comparable high ratios of exposed workers suffering from the disease with more severe clinical course. Primary principle of rehabilitation is to protect rehabilitation physicians and cover all person-to-person interactions. Translating perspectives are encouraged through each multidisciplinary approach. Rehabilitation for the outpatient remains a potential beneficial approach. Artificial intelligence can potentially provide aid and possible answers to important problems that may emerge involving COVID-19. The real value of rehabilitation in COVID-19 may be very impactful and beneficial for patient's physical and mental health.
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Affiliation(s)
- Chaoran Yu
- Department of General Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, People's Republic of China.
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Vahedian-Azimi A, Keramatfar A, Asiaee M, Atashi SS, Nourbakhsh M. Do you have COVID-19? An artificial intelligence-based screening tool for COVID-19 using acoustic parameters. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2021; 150:1945. [PMID: 34598596 PMCID: PMC8487069 DOI: 10.1121/10.0006104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/13/2021] [Accepted: 08/16/2021] [Indexed: 05/09/2023]
Abstract
This study aimed to develop an artificial intelligence (AI)-based tool for screening COVID-19 patients based on the acoustic parameters of their voices. Twenty-five acoustic parameters were extracted from voice samples of 203 COVID-19 patients and 171 healthy individuals who produced a sustained vowel, i.e., /a/, as long as they could after a deep breath. The selected acoustic parameters were from different categories including fundamental frequency and its perturbation, harmonicity, vocal tract function, airflow sufficiency, and periodicity. After the feature extraction, different machine learning methods were tested. A leave-one-subject-out validation scheme was used to tune the hyper-parameters and record the test set results. Then the models were compared based on their accuracy, precision, recall, and F1-score. Based on accuracy (89.71%), recall (91.63%), and F1-score (90.62%), the best model was the feedforward neural network (FFNN). Its precision function (89.63%) was a bit lower than the logistic regression (90.17%). Based on these results and confusion matrices, the FFNN model was employed in the software. This screening tool could be practically used at home and public places to ensure the health of each individual's respiratory system. If there are any related abnormalities in the test taker's voice, the tool recommends that they seek a medical consultant.
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Affiliation(s)
- Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | | | - Maral Asiaee
- Department of Linguistics, Faculty of Literature, Alzahra University, Tehran, Iran
| | - Seyed Shahab Atashi
- Food and Drug Control Department, Jundishapour University of Medical Sciences, Ahvaz, Iran
| | - Mandana Nourbakhsh
- Department of Linguistics, Faculty of Literature, Alzahra University, Tehran, Iran
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AlJame M, Imtiaz A, Ahmad I, Mohammed A. Deep forest model for diagnosing COVID-19 from routine blood tests. Sci Rep 2021; 11:16682. [PMID: 34404838 PMCID: PMC8371014 DOI: 10.1038/s41598-021-95957-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/03/2021] [Indexed: 02/07/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.
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Affiliation(s)
- Maryam AlJame
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait.
| | | | - Imtiaz Ahmad
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait
| | - Ameer Mohammed
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait
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Barros B, Lacerda P, Albuquerque C, Conci A. Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification. SENSORS (BASEL, SWITZERLAND) 2021; 21:5486. [PMID: 34450928 PMCID: PMC8401701 DOI: 10.3390/s21165486] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/04/2021] [Accepted: 08/05/2021] [Indexed: 12/18/2022]
Abstract
Deep Learning is a very active and important area for building Computer-Aided Diagnosis (CAD) applications. This work aims to present a hybrid model to classify lung ultrasound (LUS) videos captured by convex transducers to diagnose COVID-19. A Convolutional Neural Network (CNN) performed the extraction of spatial features, and the temporal dependence was learned using a Long Short-Term Memory (LSTM). Different types of convolutional architectures were used for feature extraction. The hybrid model (CNN-LSTM) hyperparameters were optimized using the Optuna framework. The best hybrid model was composed of an Xception pre-trained on ImageNet and an LSTM containing 512 units, configured with a dropout rate of 0.4, two fully connected layers containing 1024 neurons each, and a sequence of 20 frames in the input layer (20×2018). The model presented an average accuracy of 93% and sensitivity of 97% for COVID-19, outperforming models based purely on spatial approaches. Furthermore, feature extraction using transfer learning with models pre-trained on ImageNet provided comparable results to models pre-trained on LUS images. The results corroborate with other studies showing that this model for LUS classification can be an important tool in the fight against COVID-19 and other lung diseases.
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Affiliation(s)
- Bruno Barros
- Institute of Computing, Campus Praia Vermelha, Fluminense Federal University, Niterói 24.210-346, Brazil; (P.L.); (C.A.); (A.C.)
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Mbunge E, Fashoto SG, Akinnuwesi B, Metfula A, Simelane S, Ndumiso N. Ethics for integrating emerging technologies to contain COVID-19 in Zimbabwe. HUMAN BEHAVIOR AND EMERGING TECHNOLOGIES 2021; 3:876-890. [PMID: 34518816 PMCID: PMC8427041 DOI: 10.1002/hbe2.277] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 07/09/2021] [Accepted: 07/29/2021] [Indexed: 12/14/2022]
Abstract
Zimbabwe is among the countries affected with the coronavirus disease (COVID-19) and implemented several infection control and measures such as social distancing, contact tracing, regular temperature checking in strategic entry and exit points, face masking among others. The country also implemented recursive national lockdowns and curfews to reduce the virus transmission rate and its catastrophic impact. These large-scale measures are not easy to implement, adhere to and subsequently difficult to practice and maintain which lead to imperfect public compliance, especially if there is a significant impact on social and political norms, economy, and psychological wellbeing of the affected population. Also, emerging COVID-19 variants, porous borders, regular movement of informal traders and sale of fake vaccination certificates continue to threaten impressive progress made towards virus containment. Therefore, several emerging technologies have been adopted to strengthen the health system and health services delivery, improve compliance, adherence and maintain social distancing. These technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation, and quarantine compliance. However, the use of emerging technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which impact their efficacy, adoption and ultimately influence public trust. Therefore, the study proposes ethical framework for using emerging technologies to contain the COVID-19 pandemic. The framework is centered on ethical practices such as security, privacy, justice, human dignity, autonomy, solidarity, beneficence, and non-maleficence.
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Affiliation(s)
- Elliot Mbunge
- Department of Computer Science, Faculty of Science and Engineering University of Eswatini Manzini Swaziland.,Department of Information Technology, Faculty of Accounting and Informatics Durban University of Technology Durban South Africa
| | - Stephen G Fashoto
- Department of Computer Science, Faculty of Science and Engineering University of Eswatini Manzini Swaziland
| | - Boluwaji Akinnuwesi
- Department of Computer Science, Faculty of Science and Engineering University of Eswatini Manzini Swaziland
| | - Andile Metfula
- Department of Computer Science, Faculty of Science and Engineering University of Eswatini Manzini Swaziland
| | - Sakhile Simelane
- Department of Computer Science, Faculty of Science and Engineering University of Eswatini Manzini Swaziland
| | - Nzuza Ndumiso
- Department of Computer Science, Faculty of Science and Engineering University of Eswatini Manzini Swaziland
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42
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Acar E, Şahin E, Yılmaz İ. Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images. Neural Comput Appl 2021; 33:17589-17609. [PMID: 34345118 PMCID: PMC8321007 DOI: 10.1007/s00521-021-06344-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 07/18/2021] [Indexed: 12/12/2022]
Abstract
COVID-19 has caused a pandemic crisis that threatens the world in many areas, especially in public health. For the diagnosis of COVID-19, computed tomography has a prognostic role in the early diagnosis of COVID-19 as it provides both rapid and accurate results. This is crucial to assist clinicians in making decisions for rapid isolation and appropriate patient treatment. Therefore, many researchers have shown that the accuracy of COVID-19 patient detection from chest CT images using various deep learning systems is extremely optimistic. Deep learning networks such as convolutional neural networks (CNNs) require substantial training data. One of the biggest problems for researchers is accessing a significant amount of training data. In this work, we combine methods such as segmentation, data augmentation and generative adversarial network (GAN) to increase the effectiveness of deep learning models. We propose a method that generates synthetic chest CT images using the GAN method from a limited number of CT images. We test the performance of experiments (with and without GAN) on internal and external dataset. When the CNN is trained on real images and synthetic images, a slight increase in accuracy and other results are observed in the internal dataset, but between \documentclass[12pt]{minimal}
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\begin{document}$$9\%$$\end{document}9% in the external dataset. It is promising according to the performance results that the proposed method will accelerate the detection of COVID-19 and lead to more robust systems.
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Affiliation(s)
- Erdi Acar
- Department of Computer Engineering, Çanakkale Onsekiz Mart University, 17100 Çanakkale, Turkey
| | - Engin Şahin
- Department of Computer Engineering, Çanakkale Onsekiz Mart University, 17100 Çanakkale, Turkey
| | - İhsan Yılmaz
- Department of Computer Engineering, Çanakkale Onsekiz Mart University, 17100 Çanakkale, Turkey
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Hasan NI. A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE 2021; 1:100022. [PMID: 34337590 PMCID: PMC8299229 DOI: 10.1016/j.cmpbup.2021.100022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 07/08/2021] [Accepted: 07/20/2021] [Indexed: 05/02/2023]
Abstract
The outbreak of the SARS-CoV-2/Covid-19 virus in 2019-2020 has made the world look for fast and accurate detection methods of the disease. The most commonly used tools for detecting Covid patients are Chest-X-ray or Chest-CT-scans of the patient. However, sometimes it's hard for the physicians to diagnose the SARS-CoV-2 infection from the raw image. Moreover, sometimes, deep-learning-based techniques, using raw images, fail to detect the infection. Hence, this paper represents a hybrid method employing both traditional signal processing and deep learning technique for quick detection of SARS-CoV-2 patients based on the CT-scan and Chest-X-ray images of a patient. Unlike the other AI-based methods, here, a CT-scan/Chest-X-ray image is decomposed by two-dimensional Empirical Mode Decomposition (2DEMD), and it generates different orders of Intrinsic Mode Functions (IMFs). Next, The decomposed IMF signals are fed into a deep Convolutional Neural Network (CNN) for feature extraction and classification of Covid patients and Non-Covid patients. The proposed method is validated on three publicly available SARS-CoV-2 data sets using two deep CNN architectures. In all the databases, the modified CT-scan/Chest-X-ray image provides a better result than the raw image in terms of classification accuracy of two fundamental CNNs. This paper represents a new viewpoint of extracting preprocessed features from the raw image using 2DEMD.
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Affiliation(s)
- Nahian Ibn Hasan
- Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
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44
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Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data. ELECTRONICS 2021. [DOI: 10.3390/electronics10141626] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Research on SARS-CoV-2 and its social implications have become a major focus to interdisciplinary teams worldwide. As interest in more direct solutions, such as mass testing and vaccination grows, several studies appear to be dedicated to the operationalization of those solutions, leveraging both traditional and new methodologies, and, increasingly, the combination of both. This research examines the challenges anticipated for preventative testing of SARS-CoV-2 in schools and proposes an artificial intelligence (AI)-powered agent-based model crafted specifically for school scenarios. This research shows that in the absence of real data, simulation-based data can be used to develop an artificial intelligence model for the application of rapid assessment of school testing policies.
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Alamo T, G Reina D, Millán Gata P, Preciado VM, Giordano G. Data-driven methods for present and future pandemics: Monitoring, modelling and managing. ANNUAL REVIEWS IN CONTROL 2021; 52:448-464. [PMID: 34220287 PMCID: PMC8238691 DOI: 10.1016/j.arcontrol.2021.05.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 05/29/2023]
Abstract
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
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Affiliation(s)
- Teodoro Alamo
- Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Daniel G Reina
- Departamento de Ingeniería Electrónica, Universidad de Sevilla, Escuela Superior de Ingenieros, Sevilla, Spain
| | - Pablo Millán Gata
- Departamento de Ingeniería, Universidad Loyola Andalucía, Seville, Spain
| | - Victor M Preciado
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA
| | - Giulia Giordano
- Department of Industrial Engineering, University of Trento, Trento, Italy
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Artificial intelligence in drug design: algorithms, applications, challenges and ethics. FUTURE DRUG DISCOVERY 2021. [DOI: 10.4155/fdd-2020-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
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Kumar V, Singh D, Kaur M, Damaševičius R. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Comput Sci 2021; 7:e564. [PMID: 34141890 PMCID: PMC8176528 DOI: 10.7717/peerj-cs.564] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 05/05/2021] [Indexed: 05/09/2023]
Abstract
BACKGROUND Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. METHODOLOGY This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. RESULTS In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. CONCLUSIONS The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.
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Affiliation(s)
- Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology, Hamirpur, Himachal Pradesh, India
| | - Dilbag Singh
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Manjit Kaur
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland
- Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
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Oyelade ON, Ezugwu AES, Chiroma H. CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:77905-77919. [PMID: 36789158 PMCID: PMC8768977 DOI: 10.1109/access.2021.3083516] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 05/16/2021] [Indexed: 05/07/2023]
Abstract
The novel coronavirus, also known as COVID-19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Research into the production of relevant vaccines is progressively being advanced with the development of the Pfizer and BioNTech, AstraZeneca, Moderna, Sputnik V, Janssen, Sinopharm, Valneva, Novavax and Sanofi Pasteur vaccines. There is, however, a need for a computational intelligence solution approach to mediate the process of facilitating quick detection of the disease. Different computational intelligence methods, which comprise natural language processing, knowledge engineering, and deep learning, have been proposed in the literature to tackle the spread of coronavirus disease. More so, the application of deep learning models have demonstrated an impressive performance compared to other methods. This paper aims to advance the application of deep learning and image pre-processing techniques to characterise and detect novel coronavirus infection. Furthermore, the study proposes a framework named CovFrameNet., which consist of a pipelined image pre-processing method and a deep learning model for feature extraction, classification, and performance measurement. The novelty of this study lies in the design of a CNN architecture that incorporates an enhanced image pre-processing mechanism. The National Institutes of Health (NIH) Chest X-Ray dataset and COVID-19 Radiography database were used to evaluate and validate the effectiveness of the proposed deep learning model. Results obtained revealed that the proposed model achieved an accuracy of 0.1, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. Thus, the study's outcome showed that a CNN-based method with image pre-processing capability could be adopted for the pre-screening of suspected COVID-19 cases, and the confirmation of RT-PCR-based detected cases of COVID-19.
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Affiliation(s)
- Olaide Nathaniel Oyelade
- School of Mathematics, Statistics, and Computer ScienceUniversity of KwaZulu-Natal at PietermaritzburgPietermaritzburg3201South Africa
- Department of Computer ScienceFaculty of Physical SciencesAhmadu Bello UniversityZaria810211Nigeria
| | - Absalom El-Shamir Ezugwu
- School of Mathematics, Statistics, and Computer ScienceUniversity of KwaZulu-Natal at PietermaritzburgPietermaritzburg3201South Africa
| | - Haruna Chiroma
- Future Technology Research CenterNational Yunlin University of Science and TechnologyDouliu64002Taiwan
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Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Comput Med Imaging Graph 2021; 91:101933. [PMID: 34082281 PMCID: PMC8123377 DOI: 10.1016/j.compmedimag.2021.101933] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/15/2021] [Accepted: 04/27/2021] [Indexed: 12/13/2022]
Abstract
The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.
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Affiliation(s)
- Mustafa Alhasan
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates; Radiologic Technology Program, Applied Medical Sciences College, Jordan University of Science and Technology, Jordan.
| | - Mohamed Hasaneen
- Radiography and Medical Imaging Department, Fatima College of Health Sciences, United Arab Emirates.
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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