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Reshetnikov A, Berdutin V, Zaporozhtsev A, Romanov S, Abaeva O, Prisyazhnaya N, Vyatkina N. Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation. BMC Med Inform Decis Mak 2023; 23:48. [PMID: 36918871 PMCID: PMC10012312 DOI: 10.1186/s12911-023-02135-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 02/08/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. METHODS For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast algorithms. RESULTS Findings show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: σ(I) = 0.0129 and σ(R) = 0.0058, respectively. CONCLUSIONS The present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time.
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Affiliation(s)
- Andrey Reshetnikov
- Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation.
| | - Vitalii Berdutin
- Contract Department, Federal Budgetary Institution of Healthcare "Volga District Medical Center of the Federal Medical and Biological Agency", Nizhny Novgorod, Russian Federation
| | - Alexander Zaporozhtsev
- Department of Theoretical and Applied Mechanics, Federal State Budgetary Educational Institution of Higher Education "Nizhny Novgorod State Technical University Named After R.E. Alekseev", Nizhny Novgorod, Russian Federation
| | - Sergey Romanov
- Department of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Olga Abaeva
- Department of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Nadezhda Prisyazhnaya
- Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Nadezhda Vyatkina
- Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation
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Sharma P, Arya R, Verma R, Verma B. Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-25. [PMID: 36846527 PMCID: PMC9942051 DOI: 10.1007/s11042-023-14353-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 06/09/2022] [Accepted: 01/02/2023] [Indexed: 05/28/2023]
Abstract
Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23M parameters to train and requires fewer training samples. The proposed system is fast and robust and correctly classifies the X-Ray images into three classes, i.e. COVID-19, No Findings, and Viral Pneumonia. Experimental results on the X-Ray dataset show that our model performs well despite having fewer samples for the training and achieved an average accuracy of 96.47% for multi-class and 97.69% for binary classification on 5-fold cross-validation. The proposed model would be useful to researchers and medical professionals for assistance and prognosis for COVID-19 infected patients.
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Affiliation(s)
| | - Rhythm Arya
- Delhi Technological University, Delhi, India
| | - Richa Verma
- Delhi Technological University, Delhi, India
| | - Bindu Verma
- Delhi Technological University, Delhi, India
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3
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Thomas D, Garate D, Fu S, Bashir A, Moss N, Nair M. Telehealth reform post-public health emergency: crucial next steps. Proc AMIA Symp 2023; 36:269-271. [PMID: 36876246 PMCID: PMC9980665 DOI: 10.1080/08998280.2022.2153323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
As the pandemic made it unsafe for providers and patients to meet in person, the US government implemented key temporary telehealth waivers in March 2020 that expanded Medicare telehealth coverage dramatically. Some of the most significant changes included the removal of location restrictions so that patients and providers could engage in telehealth from their homes, full provider reimbursement for telehealth visits, coverage for more medical specialties and types of practitioners such as occupational and physical therapists, and the allowance of telehealth prescription of controlled substances. The waivers will end when the government removes the federal status of a public health emergency, which is expected to occur in 2023. Nearly 64 million Medicare patients are at risk of losing most modalities of telehealth access. We present current legislation that could combat this "telehealth cliff" and defend the position that Medicare telehealth access should remain permanently expanded.
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Affiliation(s)
- Devon Thomas
- John Sealy School of Medicine, The University of Texas Medical Branch, Galveston, Texas
| | - David Garate
- John Sealy School of Medicine, The University of Texas Medical Branch, Galveston, Texas
| | - Shangyi Fu
- School of Medicine, Baylor College of Medicine, Houston, Texas.,Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | - Amna Bashir
- School of Medicine, Baylor College of Medicine, Houston, Texas
| | - Nathaniel Moss
- School of Medicine, Baylor College of Medicine, Houston, Texas
| | - Muktha Nair
- School of Medicine, Baylor College of Medicine, Houston, Texas
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Munnangi AK, UdhayaKumar S, Ravi V, Sekaran R, Kannan S. Survival study on deep learning techniques for IoT enabled smart healthcare system. HEALTH AND TECHNOLOGY 2023; 13:215-228. [PMID: 36818549 PMCID: PMC9918340 DOI: 10.1007/s12553-023-00736-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/07/2023] [Indexed: 02/13/2023]
Abstract
Purpose The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare. Methods A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced. Results Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%. Conclusion MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.
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Affiliation(s)
- Ashok Kumar Munnangi
- Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous), Vijayawada, Andhra Pradesh India
| | - Satheeshwaran UdhayaKumar
- Department of Electronics and Communication Engineering, Pragati Engineering College, Surampalem, Andhra Pradesh India
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
| | - Ramesh Sekaran
- Department of Computer Science and Engineering, Jain University (Deemed to be University), Bangalore, Karnataka India
| | - Suthendran Kannan
- Department of Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, India
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5
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Cheong SHR, Ng YJX, Lau Y, Lau ST. Wearable technology for early detection of COVID-19: A systematic scoping review. Prev Med 2022; 162:107170. [PMID: 35878707 PMCID: PMC9304072 DOI: 10.1016/j.ypmed.2022.107170] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/29/2022] [Accepted: 07/17/2022] [Indexed: 11/23/2022]
Abstract
Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.
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Affiliation(s)
- Shing Hui Reina Cheong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Yu Jie Xavia Ng
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Siew Tiang Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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6
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Sharma A, Pruthi M, Sageena G. Adoption of telehealth technologies: an approach to improving healthcare system. TRANSLATIONAL MEDICINE COMMUNICATIONS 2022; 7:20. [PMID: 35967767 PMCID: PMC9361246 DOI: 10.1186/s41231-022-00125-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Globally, the healthcare industry is well known to be one of the strongest drivers of economic growth and development. The sector has gained substantial attention to deal with the fallout of COVID-19, leading to improvement in the quality observed in developed and developing nations. With the advent of the twenty-first century, globalization an ever-growing populace, and environmental changes prompted the more noteworthy spread of irresistible diseases, highlighting the association between wellbeing and future health security. The massive spread of COVID-19 paralyzed the global economy and took a toll on health governance and wellbeing. The present review aims to map the harrowing impacts of COVID-19 on the QoL (quality of life) observed. Particularly the post-pandemic era is likely to boot-strap the healthcare sector. Hence in post COVID era, there is a dire need to strengthen the healthcare system and understand the evolving challenges to answer calls in recovery in the wake of COVID-19. CONCLUSION There is a flurry of research highlighting the implications faced due to the rise of the pandemic, resulting in the wrecking growth and development. However, the massive potential of telehealth is still largely underexplored with scarce research on countless evolving technologies. The current crisis highlighted the need to develop emerging frameworks and facilitate multilateral cooperation. The present research can serve as the baseline for better future strategies to improve global health initiatives. Further, this can help to focus on wider health determinants, redesign strategies and policies for the healthcare industry and to mitigate/deal better with future pandemics.
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Affiliation(s)
- Arpana Sharma
- Department of Mathematics, Keshav Mahavidyalaya, University of Delhi H-4-5 Zone, Pitampura, Delhi, 110034 India
| | - Madhu Pruthi
- Principal, Keshav Mahavidyalaya, University of Delhi, H-4-5 Zone, Pitampura, Delhi, 110034 India
| | - Geetanjali Sageena
- Department of Environmental Studies, Keshav Mahavidyalaya, University of Delhi, H-4-5 Zone, Pitampura, Delhi, 110034 India
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7
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Wang W, Jiang Y, Wang X, Zhang P, Li J. Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images. BMC Med Imaging 2022; 22:135. [PMID: 35907793 PMCID: PMC9338656 DOI: 10.1186/s12880-022-00861-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: 10/26/2020] [Accepted: 07/22/2022] [Indexed: 11/23/2022] Open
Abstract
Background Corona Virus Disease 2019 (COVID-19) first appeared in December 2019, and spread rapidly around the world. COVID-19 is a pneumonia caused by novel coronavirus infection in 2019. COVID-19 is highly infectious and transmissible. By 7 May 2021, the total number of cumulative number of deaths is 3,259,033. In order to diagnose the infected person in time to prevent the spread of the virus, the diagnosis method for COVID-19 is extremely important. To solve the above problems, this paper introduces a Multi-Level Enhanced Sensation module (MLES), and proposes a new convolutional neural network model, MLES-Net, based on this module. Methods Attention has the ability to automatically focus on the key points in various information, and Attention can realize parallelism, which can replace some recurrent neural networks to a certain extent and improve the efficiency of the model. We used the correlation between global and local features to generate the attention mask. First, the feature map was divided into multiple groups, and the initial attention mask was obtained by the dot product of each feature group and the feature after the global pooling. Then the attention masks were normalized. At the same time, there were two scaling and translating parameters in each group so that the normalize operation could be restored. Then, the final attention mask was obtained through the sigmoid function, and the feature of each location in the original feature group was scaled. Meanwhile, we use different classifiers on the network models with different network layers. Results The network uses three classifiers, FC module (fully connected layer), GAP module (global average pooling layer) and GAPFC module (global average pooling layer and fully connected layer), to improve recognition efficiency. GAPFC as a classifier can obtain the best comprehensive effect by comparing the number of parameters, the amount of calculation and the detection accuracy. The experimental results show that the MLES-Net56-GAPFC achieves the best overall accuracy rate (95.27%) and the best recognition rate for COVID-19 category (100%). Conclusions MLES-Net56-GAPFC has good classification ability for the characteristics of high similarity between categories of COVID-19 X-Ray images and low intra-category variability. Considering the factors such as accuracy rate, number of network model parameters and calculation amount, we believe that the MLES-Net56-GAPFC network model has better practicability.
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Affiliation(s)
- Wei Wang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
| | - Yongbin Jiang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
| | - Xin Wang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
| | - Peng Zhang
- School of Electronics and Communications Engineering, Sun Yat-Sen University, Shenzhen, 518107, China.
| | - Ji Li
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China.
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8
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M. V. MK, Atalla S, Almuraqab N, Moonesar IA. Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey. Front Artif Intell 2022; 5:912022. [PMID: 35692941 PMCID: PMC9184735 DOI: 10.3389/frai.2022.912022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 04/26/2022] [Indexed: 12/03/2022] Open
Abstract
Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose in screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms in recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) and CT scan images. These radiographic images are rich in information such as patterns and clusters like structures, which are evident in conformance and detection of COVID-19 like pandemics. This paper aims to comprehensively study and analyze detection methodology based on Deep learning techniques for COVID-19 diagnosis. Deep learning technology is a good, practical, and affordable modality that can be deemed a reliable technique for adequately diagnosing the COVID-19 virus. Furthermore, the research determines the potential to enhance image character through artificial intelligence and distinguishes the most inexpensive and most trustworthy imaging method to anticipate dreadful viruses. This paper further discusses the cost-effectiveness of the surveyed methods for detecting COVID-19, in contrast with the other methods. Several finance-related aspects of COVID-19 detection effectiveness of different methods used for COVID-19 detection have been discussed. Overall, this study presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement.
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Affiliation(s)
- Manoj Kumar M. V.
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India
- *Correspondence: Manoj Kumar M. V.
| | - Shadi Atalla
- College of Engineering & Information Technology, University of Dubai, Dubai, United Arab Emirates
- Shadi Atalla
| | - Nasser Almuraqab
- Dubai Business School, University of Dubai, Dubai, United Arab Emirates
- Nasser Almuraqab
| | - Immanuel Azaad Moonesar
- Health Adminstration & Policy – Academic Affairs, Mohammed Bin Rashid School of Government (MBRSG), Dubai, United Arab Emirates
- Immanuel Azaad Moonesar
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Shanbehzadeh M, Yazdani A, Shafiee M, Kazemi-Arpanahi H. Predictive modeling for COVID-19 readmission risk using machine learning algorithms. BMC Med Inform Decis Mak 2022; 22:139. [PMID: 35596167 PMCID: PMC9122247 DOI: 10.1186/s12911-022-01880-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 05/18/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified. Results The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795). Conclusions Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.
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Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Azita Yazdani
- Clinical Education Research Center, Health Human Resources Research Center, Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohsen Shafiee
- Department of Nursing, Abadan University of Medical Sciences, Abadan, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran. .,Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran.
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10
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A deep learning-driven low-power, accurate, and portable platform for rapid detection of COVID-19 using reverse-transcription loop-mediated isothermal amplification. Sci Rep 2022; 12:4132. [PMID: 35260715 PMCID: PMC8903312 DOI: 10.1038/s41598-022-07954-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/28/2022] [Indexed: 12/24/2022] Open
Abstract
This paper presents a deep learning-driven portable, accurate, low-cost, and easy-to-use device to perform Reverse-Transcription Loop-Mediated Isothermal Amplification (RT-LAMP) to facilitate rapid detection of COVID-19. The 3D-printed device—powered using only a 5 Volt AC-DC adapter—can perform 16 simultaneous RT-LAMP reactions and can be used multiple times. Moreover, the experimental protocol is devised to obviate the need for separate, expensive equipment for RNA extraction in addition to eliminating sample evaporation. The entire process from sample preparation to the qualitative assessment of the LAMP amplification takes only 45 min (10 min for pre-heating and 35 min for RT-LAMP reactions). The completion of the amplification reaction yields a fuchsia color for the negative samples and either a yellow or orange color for the positive samples, based on a pH indicator dye. The device is coupled with a novel deep learning system that automatically analyzes the amplification results and pays attention to the pH indicator dye to screen the COVID-19 subjects. The proposed device has been rigorously tested on 250 RT-LAMP clinical samples, where it achieved an overall specificity and sensitivity of 0.9666 and 0.9722, respectively with a recall of 0.9892 for Ct < 30. Also, the proposed system can be widely used as an accurate, sensitive, rapid, and portable tool to detect COVID–19 in settings where access to a lab is difficult, or the results are urgently required.
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Jawahar M, Prassanna J, Ravi V, Anbarasi LJ, Jasmine SG, Manikandan R, Sekaran R, Kannan S. Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:40451-40468. [PMID: 35572385 PMCID: PMC9090123 DOI: 10.1007/s11042-022-13183-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 01/30/2022] [Accepted: 04/28/2022] [Indexed: 05/13/2023]
Abstract
The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic disease from spreading. Computed tomography (CT) is a diagnostic tool used by radiologists to treat COVID patients. COVID x-ray images have inherent texture variations and similarity to other diseases like pneumonia. Manually diagnosing COVID X-ray images is a tedious and challenging process. Extracting the discriminant features and fine-tuning the classifiers using low-resolution images with a limited COVID x-ray dataset is a major challenge in computer aided diagnosis. The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) classifier. The proposed HOG feature extraction method is evaluated with Gray-Level Co-Occurrence Matrix (GLCM) and Hu moments. Results confirm that HOG is found to reflect the local description of edges effectively and provide excellent structural features to discriminate COVID and non-COVID when compared to the other feature extraction techniques. The performance of the RF is compared with other classifiers such as Linear Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbor (kNN), Classification and Regression Trees (CART), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer perceptron neural network (MLP). Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.
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Affiliation(s)
- Malathy Jawahar
- Leather Process Technology Division, CSIR-Central Leather Research Institute, Adyar, Chennai, 600020 India
| | - J. Prassanna
- School of Computer Science and Engineering, Vellore Institute of Technology, 600 127 Chennai, India
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
| | - L. Jani Anbarasi
- School of Computer Science and Engineering, Vellore Institute of Technology, 600 127 Chennai, India
| | - S. Graceline Jasmine
- School of Computer Science and Engineering, Vellore Institute of Technology, 600 127 Chennai, India
| | - R. Manikandan
- School of Computing, SASTRA Deemed University, Thanjavur, India
| | - Ramesh Sekaran
- Department of Information Technology, Velgapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
| | - Suthendran Kannan
- Department of Information Technology, Kalasalingam Academy of Research and Education, Srivilliputhur, India
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12
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Sitaula C, Shahi TB, Aryal S, Marzbanrad F. Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection. Sci Rep 2021; 11:23914. [PMID: 34903792 PMCID: PMC8668931 DOI: 10.1038/s41598-021-03287-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 11/29/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer’s output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: \documentclass[12pt]{minimal}
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\begin{document}$$3 \times 3$$\end{document}3×3. We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively).
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Affiliation(s)
- Chiranjibi Sitaula
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC, 3800, Australia.
| | - Tej Bahadur Shahi
- School of Engineering and Technology, Central Queensland University, Rockhampton, QLD, 4701, Australia.,School of Information Technology, Deakin University, Waurn Ponds, VIC, 3216, Australia
| | - Sunil Aryal
- Central Department of Computer Science and IT, Tribhuvan University, Kathmandu, 44600, Nepal
| | - Faezeh Marzbanrad
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC, 3800, Australia
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13
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Miao R, Dong X, Xie SL, Liang Y, Lo SL. UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients. BMC Med Imaging 2021; 21:174. [PMID: 34809589 PMCID: PMC8607405 DOI: 10.1186/s12880-021-00704-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 11/10/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. METHODS This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. RESULTS The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models. CONCLUSION In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.
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Affiliation(s)
- Rui Miao
- Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
- Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Xin Dong
- Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Sheng-Li Xie
- Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, 510006, China
| | - Yong Liang
- Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
- Department of State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Sio-Long Lo
- Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
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14
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Al-Ali A, Elharrouss O, Qidwai U, Al-Maaddeed S. ANFIS-Net for automatic detection of COVID-19. Sci Rep 2021; 11:17318. [PMID: 34453082 PMCID: PMC8397755 DOI: 10.1038/s41598-021-96601-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/04/2021] [Indexed: 12/24/2022] Open
Abstract
Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.
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Affiliation(s)
- Afnan Al-Ali
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
| | - Omar Elharrouss
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Uvais Qidwai
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Somaya Al-Maaddeed
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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15
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Wang J, Liu C, Li J, Yuan C, Zhang L, Jin C, Xu J, Wang Y, Wen Y, Lu H, Li B, Chen C, Li X, Shen D, Qian D, Wang J. iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients. NPJ Digit Med 2021; 4:124. [PMID: 34400751 PMCID: PMC8367981 DOI: 10.1038/s41746-021-00496-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.
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Affiliation(s)
- Jun Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jingwen Li
- Department of Gastroenterology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Cheng Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lichi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng Jin
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jianwei Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yaqi Wang
- College of Media, Communication University of Zhejiang, Hangzhou, China
| | - Yaofeng Wen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hongbing Lu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiangdong Li
- Department of Radiology, General Hospital of Southern Theatre Command, PLA, Guangzhou, China.
- Department of Radiology, Huoshenshan Hospital, Wuhan, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, China.
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai, China.
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
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16
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Hou J, Gao T. Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection. Sci Rep 2021; 11:16071. [PMID: 34373554 PMCID: PMC8352869 DOI: 10.1038/s41598-021-95680-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 07/28/2021] [Indexed: 02/07/2023] Open
Abstract
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.
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Affiliation(s)
- Jie Hou
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, Guangdong, China
| | - Terry Gao
- Counties Manukau District Health Board, Auckland, 1640, New Zealand.
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17
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Sengupta K, Srivastava PR. Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients. BMC Med Inform Decis Mak 2021; 21:227. [PMID: 34330278 PMCID: PMC8323083 DOI: 10.1186/s12911-021-01588-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 07/18/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. METHODS This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. RESULTS The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. CONCLUSION The results suggest that quantum neural networks outperform in COVID-19 traits' classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.
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Affiliation(s)
- Kinshuk Sengupta
- Microsoft Corporation, New Delhi
, India
- Department of Information System, Indian Institute of Management, Rohtak, India
- City Southern Bypass, Sunaria, Rohtak, Haryana 124010 India
| | - Praveen Ranjan Srivastava
- Department of Information System, Indian Institute of Management, Rohtak, India
- City Southern Bypass, Sunaria, Rohtak, Haryana 124010 India
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18
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Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Gorriz JM, Hussain S, Sani ZA, Moosaei H, Khosravi A, Nahavandi S, Islam SMS. Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 2021; 11:15343. [PMID: 34321491 PMCID: PMC8319175 DOI: 10.1038/s41598-021-93543-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/25/2021] [Indexed: 02/07/2023] Open
Abstract
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
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Affiliation(s)
- Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Navid Hoseini Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | | | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia.
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam, 786004, India
| | | | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, 3220, Australia
- Cardiovascular Division, The George Institute for Global Health, Newtown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
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19
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Shah K, Tomljenovic-Berube A. A New Dimension of Health Care: The Benefits, Limitations and Implications of Virtual Medicine. JOURNAL OF UNDERGRADUATE LIFE SCIENCES 2021. [DOI: 10.33137/juls.v15i1.37034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background: Virtual medicine has been rapidly evolving over the past several decades. However, obstacles such as data security, inadequate funding and limited technological resources have hindered its seamless incorporation into the health care system. The recent pandemic has induced a widespread adoption of virtual care practices to remove the need for physical meetings between patients and health care practitioners.
Purpose: This literature review aims to examine the current state of virtual medicine amid the COVID-19 pandemic and evaluate the benefits, limitations and implications of continuing technological advancements in the future.
Findings: Most of the available literature suggests that the recent adoption of virtual medicine has allowed practitioners to cut down on costs and secondary expenses while maintaining the quality of medical care services. Due to the growing consumer demand, researchers predict that virtual medicine may be a viable modality for patient care post-pandemic. However, concerns surrounding patient security and digital infrastructure threaten the ability of virtual medicine to provide quality and effective health care. Additionally, rural virtual medicine programs face challenges in expanding services due to the scarcity of information and communication technology specialists and inadequate funding. Comprehensive legislation and governance standards must be implemented to ensure proper data security and privacy. Additional funds may also be required to train staff, reform current digital software and improve the quality of service. The proliferation of advanced technologies and improvements in current platforms will enable more providers to render virtual medical care services.
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20
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Rahman MM, Islam MM, Manik MMH, Islam MR, Al-Rakhami MS. Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic. ACTA ACUST UNITED AC 2021; 2:384. [PMID: 34308367 PMCID: PMC8287848 DOI: 10.1007/s42979-021-00774-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/11/2021] [Indexed: 12/24/2022]
Abstract
Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.
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Affiliation(s)
- Mohammad Marufur Rahman
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md Motaleb Hossen Manik
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md Rabiul Islam
- Department of Electrical and Electronic Engineering, Bangladesh Army University of Engineering and Technology, Natore, 6431 Bangladesh
| | - Mabrook S Al-Rakhami
- Research Chair of Pervasive and Mobile Computing, Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
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21
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Zhao W, Jiang W, Qiu X. Deep learning for COVID-19 detection based on CT images. Sci Rep 2021; 11:14353. [PMID: 34253822 PMCID: PMC8275612 DOI: 10.1038/s41598-021-93832-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/18/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.
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Affiliation(s)
- Wentao Zhao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
- School of Intelligent Transportation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, China
| | - Wei Jiang
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Xinguo Qiu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
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