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Gheisari M, Ghaderzadeh M, Li H, Taami T, Fernández-Campusano C, Sadeghsalehi H, Afzaal Abbasi A. Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review. JMIR Mhealth Uhealth 2024; 12:e44406. [PMID: 38231538 PMCID: PMC10896318 DOI: 10.2196/44406] [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: 11/18/2022] [Revised: 01/02/2023] [Accepted: 08/18/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. OBJECTIVE With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. METHODS In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. RESULTS Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)-based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. CONCLUSIONS Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.
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Affiliation(s)
- Mehdi Gheisari
- Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
| | - Mustafa Ghaderzadeh
- School of Nursing and Health Sciences of Boukan, Urmia University of Medical Sciences, Urmia, Iran
| | - Huxiong Li
- Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China
| | - Tania Taami
- Florida State University, Tallahassee, FL, United States
| | | | | | - Aaqif Afzaal Abbasi
- Department of Earth and Marine Sciences, University of Palermo, Palermo, Italy
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Modjo R, Lestari F, Tanjung H, Kadir A, Putra RS, Rahmadani M, Chaeruman AS, Lestari F, Sutanto J. COVID-19 infection prevention and control for hospital workers in Indonesia. Front Public Health 2024; 11:1276898. [PMID: 38259732 PMCID: PMC10800904 DOI: 10.3389/fpubh.2023.1276898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction The outbreak of SARS-CoV-2 in 2019 led to a global pandemic, posing unprecedented challenges to healthcare systems, particularly in hospitals. Purpose This study explores the intricacies of strategies employed for preventing and controlling COVID-19 in Indonesian hospitals, with a particular focus on the protocols, challenges, and solutions faced by healthcare professionals. Methods Using a cross-sectional analysis, we examined 27 hospitals and uncovered disparities in their preparedness levels. During our investigation, we observed the robust implementation of infection prevention measures, which encompassed stringent protocols, adequate ventilation, and proper use of personal protective equipment. However, shortcomings were identified in areas such as surveillance, mental health support, and patient management. Discussion This study underscores the importance of addressing these gaps, suggesting tailored interventions, and continuous training for healthcare staff. Effective leadership, positive team dynamics, and adherence to comprehensive policies emerge as pivotal factors. Hospitals should strengthen weak areas, ensure the ethical execution of emergency protocols, and integrate technology for tracking and improving standard operating procedures. By enhancing the knowledge and skills of healthcare workers and maintaining strong management practices, hospitals can optimize their efforts in COVID-19 prevention and control, thereby safeguarding the wellbeing of professionals, patients, and communities.
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Affiliation(s)
- Robiana Modjo
- Occupational Health and Safety Department, Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia
- Indonesia Occupational Health Experts Association, Jakarta, Indonesia
| | - Fatma Lestari
- Occupational Health and Safety Department, Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia
- Disaster Risk Reduction Center, Universitas Indonesia, Depok, West Java, Indonesia
| | - Hendra Tanjung
- Occupational Health and Safety Department, Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia
- Indonesia Occupational Health Experts Association, Jakarta, Indonesia
| | - Abdul Kadir
- Occupational Health and Safety Department, Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia
| | | | - Meilisa Rahmadani
- Indonesia Occupational Health Experts Association, Jakarta, Indonesia
- Universitas Indonesia Hospital, Depok, West Java, Indonesia
| | | | - Fetrina Lestari
- Indonesia Occupational Health Experts Association, Jakarta, Indonesia
| | - Juliana Sutanto
- Department of Human Centred Computing, Monash University, Melbourne, VIC, Australia
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Nur-A-Alam M, Nasir MK, Ahsan M, Based MA, Haider J, Kowalski M. Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN. Sci Rep 2023; 13:20063. [PMID: 37973820 PMCID: PMC10654719 DOI: 10.1038/s41598-023-47183-9] [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: 03/06/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023] Open
Abstract
The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.
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Affiliation(s)
- Md Nur-A-Alam
- Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Mostofa Kamal Nasir
- Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, York, YO10 5GH, UK
| | - Md Abdul Based
- Department of Computer Science & Engineering, Dhaka International University, Dhaka, 1205, Bangladesh
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester St, Manchester, M1 5GD, UK
| | - Marcin Kowalski
- Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, Poland.
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Eshraghi MA, Ayatollahi A, Shokouhi SB. COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images. BMC Med Imaging 2023; 23:83. [PMID: 37322450 PMCID: PMC10273540 DOI: 10.1186/s12880-023-01039-w] [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: 03/14/2023] [Accepted: 06/01/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND The medical profession is facing an excessive workload, which has led to the development of various Computer-Aided Diagnosis (CAD) systems as well as Mobile-Aid Diagnosis (MAD) systems. These technologies enhance the speed and accuracy of diagnoses, particularly in areas with limited resources or remote regions during the pandemic. The primary purpose of this research is to predict and diagnose COVID-19 infection from chest X-ray images by developing a mobile-friendly deep learning framework, which has the potential for deployment in portable devices such as mobile or tablet, especially in situations where the workload of radiology specialists may be high. Moreover, this could improve the accuracy and transparency of population screening to assist radiologists during the pandemic. METHODS In this study, the Mobile Networks ensemble model called COV-MobNets is proposed to classify positive COVID-19 X-ray images from negative ones and can have an assistant role in diagnosing COVID-19. The proposed model is an ensemble model, combining two lightweight and mobile-friendly models: MobileViT based on transformer structure and MobileNetV3 based on Convolutional Neural Network. Hence, COV-MobNets can extract the features of chest X-ray images in two different methods to achieve better and more accurate results. In addition, data augmentation techniques were applied to the dataset to avoid overfitting during the training process. The COVIDx-CXR-3 benchmark dataset was used for training and evaluation. RESULTS The classification accuracy of the improved MobileViT and MobileNetV3 models on the test set has reached 92.5% and 97%, respectively, while the accuracy of the proposed model (COV-MobNets) has reached 97.75%. The sensitivity and specificity of the proposed model have also reached 98.5% and 97%, respectively. Experimental comparison proves the result is more accurate and balanced than other methods. CONCLUSION The proposed method can distinguish between positive and negative COVID-19 cases more accurately and quickly. The proposed method proves that utilizing two automatic feature extractors with different structures as an overall framework of COVID-19 diagnosis can lead to improved performance, enhanced accuracy, and better generalization to new or unseen data. As a result, the proposed framework in this study can be used as an effective method for computer-aided diagnosis and mobile-aided diagnosis of COVID-19. The code is available publicly for open access at https://github.com/MAmirEshraghi/COV-MobNets .
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Affiliation(s)
- Mohammad Amir Eshraghi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ahmad Ayatollahi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
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Sfayyih AH, Sulaiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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Shareef AQ, Kurnaz S. Deep Learning Based COVID-19 Detection via Hard Voting Ensemble Method. WIRELESS PERSONAL COMMUNICATIONS 2023:1-12. [PMID: 37360134 PMCID: PMC10170044 DOI: 10.1007/s11277-023-10485-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/24/2023] [Indexed: 06/28/2023]
Abstract
Healthcare systems throughout the world are under a great deal of strain because to the continuing COVID-19 epidemic, making early and precise diagnosis critical for limiting the virus's propagation and efficiently treating victims. The utilization of medical imaging methods like X-rays can help to speed up the diagnosis procedure. Which can offer valuable insights into the virus's existence in the lungs. We present a unique ensemble approach to identify COVID-19 using X-ray pictures (X-ray-PIC) in this paper. The suggested approach, based on hard voting, combines the confidence scores of three classic deep learning models: CNN, VGG16, and DenseNet. We also apply transfer learning to enhance performance on small medical image datasets. Experiments indicate that the suggested strategy outperforms current techniques with a 97% accuracy, a 96% precision, a 100% recall, and a 98% F1-score.These results demonstrate the effectiveness of using ensemble approaches and COVID-19 transfer-learning diagnosis using X-ray-PIC, which could greatly aid in early detection and reducing the burden on global health systems.
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Affiliation(s)
- Asaad Qasim Shareef
- Department of Electrical Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Sefer Kurnaz
- Department of Electrical Computer Engineering, Altinbas University, Istanbul, Turkey
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Gheisari M, Ebrahimzadeh F, Rahimi M, Moazzamigodarzi M, Liu Y, Dutta Pramanik PK, Heravi MA, Mehbodniya A, Ghaderzadeh M, Feylizadeh MR, Kosari S. Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Affiliation(s)
- Mehdi Gheisari
- School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China
- Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India
- Department of Computer Science Islamic Azad University Tehran Iran
| | | | - Mohamadtaghi Rahimi
- Department of Mathematics and Statistics Iran University of Science and Technology Tehran Iran
| | | | - Yang Liu
- School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China
- Peng Cheng Laboratory Shenzhen China
| | | | | | - Abolfazl Mehbodniya
- Department of Electronics and Communications Engineering Kuwait College of Science and Technology Doha District Kuwait
| | - Mustafa Ghaderzadeh
- Department of Artificial Intelligence Smart University of Medical Sciences Tehran Iran
| | | | - Saeed Kosari
- Institute of Computing Science and Technology, Guangzhou University Guangzhou China
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8
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Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Yoma NB. Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone. SENSORS (BASEL, SWITZERLAND) 2023; 23:2441. [PMID: 36904646 PMCID: PMC10007248 DOI: 10.3390/s23052441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/08/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were designed, or chosen, to deal with the stationary noise suppression of cellular handsets, to provoke different rates of exhaled air, and to stimulate different levels of fluency. Time-independent and time-dependent engineered features were proposed and selected, and a k-fold scheme with double validation was adopted to select the models with the greatest potential for generalization. Moreover, score fusion methods were also investigated to optimize the complementarity of the controlled phonetizations and features that were engineered and selected. The results reported here were obtained from 104 participants, where 34 corresponded to healthy individuals and 70 were patients with respiratory conditions. The subjects' vocalizations were recorded with a telephone call (i.e., with an IVR server). The system provided an accuracy of 59% (i.e., estimating the correct mMRC), a root mean square error equal to 0.98, false positive rate of 6%, false negative rate of 11%, and an area under the ROC curve equal to 0.97. Finally, a prototype was developed and implemented, with an ASR-based automatic segmentation scheme, to estimate dyspnea on line.
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Affiliation(s)
- Eduardo Alvarado
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Nicolás Grágeda
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Alejandro Luzanto
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Rodrigo Mahu
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Jorge Wuth
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Laura Mendoza
- Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Néstor Becerra Yoma
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
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Ali F, Ullah F, Khan JI, Khan J, Sardar AW, Lee S. COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm. CHAOS, SOLITONS, AND FRACTALS 2023; 167:112984. [PMID: 36530380 PMCID: PMC9744690 DOI: 10.1016/j.chaos.2022.112984] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 10/14/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
Many severe epidemics and pandemics have hit human civilizations throughout history. The recent Sever Actuate Respiratory disease SARS-CoV-2 known as COVID-19 became a global disease and is still growing around the globe. It has severely affected the world's economy and ways of life. It necessitates predicting the spread in advance and considering various control policies to avoid the country's complete closure. In this paper, we propose deep learning-based stacked Bi-directional long short-term memory (Stacked Bi-LSTM) network that forecasts COVID-19 more accurately for the country of South Korea. The paper's main objectives are to present a lightweight, accurate, and optimized model to predict the spread considering restriction policies such as school closure, workspace closing, and the canceling of public events. Based on the fourteen parameters (including control policies), we predict and forecast the future value of the number of positive, dead, recovered, and quarantined cases. In this paper, we use the dataset of South Korea comprised of several control policies implemented for minimizing the spread of COVID-19. We compare the performance of the stacked Bi-LSTM with the traditional time-series models and LSTM model using the performance metrics mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, we study the impact of control policies on forecasting accuracy. We further study the impact of changing the Bi-LSTM default activation functions Tanh with ReLU on forecasting accuracy. The research provides insight to policymakers to optimize the pooling of resources more optimally on the correct date and time prior to the event and to control the spread by employing various strategies in the meantime.
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Affiliation(s)
- Furqan Ali
- School of Electronics and Information Engineering, Korea Aerospace University, Deogyang-gu, Goyang-si 412-791, Gyeonggi-do, South Korea
| | - Farman Ullah
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan
| | - Junaid Iqbal Khan
- School of Electronics and Information Engineering, Korea Aerospace University, Deogyang-gu, Goyang-si 412-791, Gyeonggi-do, South Korea
| | - Jebran Khan
- School of Electronics and Information Engineering, Korea Aerospace University, Deogyang-gu, Goyang-si 412-791, Gyeonggi-do, South Korea
- Department of Artificial Intelligence, AJOU University, South Korea
| | - Abdul Wasay Sardar
- School of Electronics and Information Engineering, Korea Aerospace University, Deogyang-gu, Goyang-si 412-791, Gyeonggi-do, South Korea
| | - Sungchang Lee
- School of Electronics and Information Engineering, Korea Aerospace University, Deogyang-gu, Goyang-si 412-791, Gyeonggi-do, South Korea
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Khan A, Khan SH, Saif M, Batool A, Sohail A, Waleed Khan M. A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2165724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Asifullah Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
- Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
| | - Saddam Hussain Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan
| | - Mahrukh Saif
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Asiya Batool
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
| | - Anabia Sohail
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Computer Science, Faculty of Computing & Artificial Intelligence, Air University, Islamabad, Pakistan
| | - Muhammad Waleed Khan
- Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan
- Department of Mechanical and Aerospace Engineering, Columbus, OH, USA
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Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN COMPUTER SCIENCE 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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Naseem MT, Hussain T, Lee CS, Khan MA. Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:7977. [PMID: 36298328 PMCID: PMC9610066 DOI: 10.3390/s22207977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/17/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.
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Affiliation(s)
- Muhammad Tahir Naseem
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
| | - Tajmal Hussain
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
| | - Chan-Su Lee
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Muhammad Adnan Khan
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
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Carrillo-Larco RM, Castillo-Cara M, Hernández Santa Cruz JF. Street images classification according to COVID-19 risk in Lima, Peru: a convolutional neural networks feasibility analysis. BMJ Open 2022; 12:e063411. [PMID: 36123096 PMCID: PMC9485648 DOI: 10.1136/bmjopen-2022-063411] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 09/01/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES During the COVID-19 pandemic, convolutional neural networks (CNNs) have been used in clinical medicine (eg, X-rays classification). Whether CNNs could inform the epidemiology of COVID-19 classifying street images according to COVID-19 risk is unknown, yet it could pinpoint high-risk places and relevant features of the built environment. In a feasibility study, we trained CNNs to classify the area surrounding bus stops (Lima, Peru) into moderate or extreme COVID-19 risk. DESIGN CNN analysis based on images from bus stops and the surrounding area. We used transfer learning and updated the output layer of five CNNs: NASNetLarge, InceptionResNetV2, Xception, ResNet152V2 and ResNet101V2. We chose the best performing CNN, which was further tuned. We used GradCam to understand the classification process. SETTING Bus stops from Lima, Peru. We used five images per bus stop. PRIMARY AND SECONDARY OUTCOME MEASURES Bus stop images were classified according to COVID-19 risk into two labels: moderate or extreme. RESULTS NASNetLarge outperformed the other CNNs except in the recall metric for the moderate label and in the precision metric for the extreme label; the ResNet152V2 performed better in these two metrics (85% vs 76% and 63% vs 60%, respectively). The NASNetLarge was further tuned. The best recall (75%) and F1 score (65%) for the extreme label were reached with data augmentation techniques. Areas close to buildings or with people were often classified as extreme risk. CONCLUSIONS This feasibility study showed that CNNs have the potential to classify street images according to levels of COVID-19 risk. In addition to applications in clinical medicine, CNNs and street images could advance the epidemiology of COVID-19 at the population level.
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Affiliation(s)
- Rodrigo M Carrillo-Larco
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Universidad Continental, Lima, Peru
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Laddha S, Mnasri S, Alghamdi M, Kumar V, Kaur M, Alrashidi M, Almuhaimeed A, Alshehri A, Alrowaily MA, Alkhazi I. COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study. Diagnostics (Basel) 2022; 12:1880. [PMID: 36010231 PMCID: PMC9406661 DOI: 10.3390/diagnostics12081880] [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: 05/05/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.
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Affiliation(s)
- Saloni Laddha
- Computer Science and Engineering Department, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India; (S.L.); (V.K.)
| | - Sami Mnasri
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
- Department of Computer Science, ISSAT of Gafsa, University of Gafsa, Gafsa 2112, Tunisia
| | - Mansoor Alghamdi
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India; (S.L.); (V.K.)
| | - Manjit Kaur
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
| | - Malek Alrashidi
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Abdullah Almuhaimeed
- The National Centre for Genomics Technologies and Bioinformatics, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
| | - Ali Alshehri
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Majed Abdullah Alrowaily
- Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia; (M.A.); (M.A.); (A.A.); (M.A.A.)
| | - Ibrahim Alkhazi
- College of Computers & Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia;
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Siddiqui S, Arifeen M, Hopgood A, Good A, Gegov A, Hossain E, Rahman W, Hossain S, Al Jannat S, Ferdous R, Masum S. Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review. SN COMPUTER SCIENCE 2022; 3:397. [PMID: 35911439 PMCID: PMC9312319 DOI: 10.1007/s42979-022-01326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/11/2022] [Indexed: 10/29/2022]
Abstract
COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.
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Affiliation(s)
- Shah Siddiqui
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK.,School of Computing, University of Portsmouth (UoP), Lion Terrace, Portsmouth, PO1 3HE UK
| | - Murshedul Arifeen
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Adrian Hopgood
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alice Good
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alexander Gegov
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Elias Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Wahidur Rahman
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shazzad Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Sabila Al Jannat
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Rezowan Ferdous
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shamsul Masum
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
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16
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Ahmed MM, Sayed AM, El Abd D, El Sayed IT, Elkholy YS, Fares AH, Fares S. Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals. J Int Med Res 2022; 50:3000605221109392. [PMID: 35861236 PMCID: PMC9310293 DOI: 10.1177/03000605221109392] [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] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVES Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to correlate clinical and laboratory data of patients with COVID-19 and their polymerase chain reaction (PCR) results and to assess the accuracy of a deep learning model in diagnosing COVID-19. METHODS This was a retrospective study using an anonymized dataset of patients with suspected COVID-19. Only patients with a complete dataset were included (n = 440). A deep analytics framework and dual-modal approach for PCR-based classification was used, integrating symptoms and laboratory-based modalities. RESULTS Participants with loss of smell or taste were two times more likely to have positive PCR results (odds ratio [OR] 1.86). Participants with neutropenia, high serum ferritin, or monocytosis were three, four, and five times more likely to have positive PCR results (OR 2.69, 4.18, 5.42, respectively). The rate of accuracy achieved using the deep learning framework was 78%, with sensitivity of 83.9% and specificity of 71.4%. CONCLUSION Loss of smell or taste, neutropenia, monocytosis, and high serum ferritin should be routinely assessed with suspected COVID-19 infection. The use of deep learning for diagnosis is a promising tool that can be implemented in the primary care setting.
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Affiliation(s)
- Marwa M Ahmed
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Amal M Sayed
- Department of Clinical & Chemical Pathology, Kasralainy, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Dina El Abd
- Department of Clinical & Chemical Pathology, Kasralainy, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Inas T El Sayed
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Yasmine S Elkholy
- Department of Medical Microbiology & Immunology, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ahmed H Fares
- Computer Science and Engineering Department, Faculty of Engineering, Benha University, Cairo, Egypt
| | - Samar Fares
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
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17
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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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18
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Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106731. [PMID: 35286874 PMCID: PMC8897838 DOI: 10.1016/j.cmpb.2022.106731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.
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Affiliation(s)
- Haseeb Hassan
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
| | - Zhaoyu Ren
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Chengmin Zhou
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Jian Zhao
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Bingding Huang
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
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19
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Vaidyanathan A, Guiot J, Zerka F, Belmans F, Van Peufflik I, Deprez L, Danthine D, Canivet G, Lambin P, Walsh S, Occchipinti M, Meunier P, Vos W, Lovinfosse P, Leijenaar RT. An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest CT. ERJ Open Res 2022; 8:00579-2021. [PMID: 35509437 PMCID: PMC8958945 DOI: 10.1183/23120541.00579-2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/04/2022] [Indexed: 01/08/2023] Open
Abstract
Purpose In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza. A fully automated artificial intelligence-based network is proposed to classify CT volumes of patients affected with COVID-19 or influenza/CAP, and in the uninfectedhttps://bit.ly/3MJrVRi
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20
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Almasi S, Shahbodaghi A, Asadi F. Efficacy of Telemedicine for the Management of Asthma: A Systematic Review. TANAFFOS 2022; 21:132-145. [PMID: 36879729 PMCID: PMC9985125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/15/2021] [Indexed: 03/08/2023]
Abstract
Background Considering the increased prevalence of asthma and its consequences for individuals and society, its effective management and close monitoring is essential. Awareness of the effects of telemedicine can improve asthma management. The present study aimed to systematically review articles examining the effect of telemedicine on the management of asthma, including control of the symptom, patients' quality of life, costs, and adherence to treatment programs. Materials and Methods A systematic search was performed on four databases: PubMed, Web of Science, Embase, and Scopus. English language clinical trials investigating the effectiveness of telemedicine in asthma management published from 2005 to 2018 were selected and retrieved. The present study was designed and conducted based on the PRISMA guidelines. Results Out of 33 articles included in this research, telemedicine was employed by 23 studies for the promotion of patient adherence to treatment in the form of reminders and feedback, by 18 for telemonitoring and communicating with healthcare providers, by six for offering remote patient education, and by five for counseling. The most frequently used telemedicine approach was asynchronous (used in 21 articles), and the most commonly utilized tool was Web-based (utilized in 11 articles). Conclusion Telemedicine can improve symptom control, patients' quality of life, and adherence to treatment programs. However, little evidence exists confirming the effectiveness of telemedicine in decreasing costs.
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Affiliation(s)
- Sohrab Almasi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azam Shahbodaghi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Department of Medical Library and Information Science, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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21
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Khurana Batra P, Aggarwal P, Wadhwa D, Gulati M. Predicting pattern of coronavirus using X-ray and CT scan images. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022; 11:39. [PMID: 36212780 PMCID: PMC9532815 DOI: 10.1007/s13721-022-00382-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 08/06/2022] [Accepted: 09/21/2022] [Indexed: 11/03/2022]
Abstract
Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world's central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.
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Affiliation(s)
- Payal Khurana Batra
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
| | - Paras Aggarwal
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
| | - Dheeraj Wadhwa
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
| | - Mehul Gulati
- grid.419639.00000 0004 1772 7740Department of CSE and IT, Jaypee Institute of Information Technology, Noida, India
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22
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Thimoteo LM, Vellasco MM, Amaral J, Figueiredo K, Yokoyama CL, Marques E. Explainable Artificial Intelligence for COVID-19 Diagnosis Through Blood Test Variables. JOURNAL OF CONTROL, AUTOMATION AND ELECTRICAL SYSTEMS 2022; 33. [PMCID: PMC8722647 DOI: 10.1007/s40313-021-00858-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
This work proposes an explainable artificial intelligence approach to help diagnose COVID-19 patients based on blood test and pathogen variables. Two glass-box models, logistic regression and explainable boosting machine, and two black-box models, random forest and support vector machine, were used to assess the disease diagnosis. Shapley additive explanations were used to explain predictions for the black-box models, while glass-box models feature importance brought insights into the most relevant features. All global explanations show the eosinophils and leukocytes, white blood cells are among the essential features to help diagnose the COVID-19. Moreover, the best model obtained an AUC of 0.87.
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Affiliation(s)
- Lucas M. Thimoteo
- Departamento de Engenharia Elétrica, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Marley M. Vellasco
- Departamento de Engenharia Elétrica, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Jorge Amaral
- Programa de Pós-Graduação em Engenharia Eletrônica (PEL), Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Karla Figueiredo
- Programa de Pós-Graduação em Ciências Computacionais (CCOMP), Programa de Pós-Graduação em Telessaúde, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Cátia Lie Yokoyama
- Departamento de Biologia Geral, Universidade Estadual de Londrina, Londrina, PR Brasil
| | - Erito Marques
- Programa de Pós-Graduação em Engenharia Eletrônica (PEL), Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ Brasil
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23
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Haq IU, Du X, Jan H. Implementation of smart social distancing for COVID-19 based on deep learning algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:33569-33589. [PMID: 35463218 PMCID: PMC9019287 DOI: 10.1007/s11042-022-13154-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 12/22/2021] [Accepted: 04/10/2022] [Indexed: 05/12/2023]
Abstract
The first step to reducing the effect of viral disease is to prevent the spread which could be achieved by implementing social distancing (reducing the number of close physical interactions between peoples). Almost every viral disease whose means of communication is air, and enters through mouth or nose, definitely will affect our vocal organs which cause changes in features of our voice and could be traceable using feature analysis of voice using deep learning. The detection of an affected person using deep neural networks and tracking him would help us in the implementation of the social distancing rule in an area where it is needed. The aim of this paper is to study different solutions which help in enabling, encouraging, and even enforcing social distancing. In this paper, we implemented and analyzed scenarios on the basis of COVID-19 patient detection using cough and tracking him using smart cameras, or emerging wireless technologies with deep learning techniques for prediction and preventing the spread of disease. Thus these techniques are easy to be implemented in the initial stage of any pandemic as well and will help us in the implementation of smart social distancing (apply whenever needed).
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Affiliation(s)
- Izaz Ul Haq
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Xianjun Du
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Haseeb Jan
- University of Engineering and Technology, Peshawar, Pakistan
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Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5040073] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19.
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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: 10] [Impact Index Per Article: 3.3] [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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Corrigendum to "Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review". JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9868517. [PMID: 34733462 PMCID: PMC8560226 DOI: 10.1155/2021/9868517] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 09/29/2021] [Indexed: 11/18/2022]
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Junior CP, Cavallieri GV, da Silva FA, Fernandes GL, Nai GA, Salge AKM, Puhle JG, de Resende E Silva DT, Pereira DR, de Azevedo Mello F, Favareto APA, Rossi RC. Digital image processing: a useful tool in the analysis of lung injuries caused by chronic inhalation of agricultural herbicides. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:57918-57924. [PMID: 34097223 PMCID: PMC8183328 DOI: 10.1007/s11356-021-14692-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 05/31/2021] [Indexed: 06/12/2023]
Abstract
The herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) is widely used in agriculture to control various weeds. The objective of this study was to use the digital image processing method to identify alveolar lesions in the lungs of rats submitted to chronic 2,4-dichlorophenoxyacetic acid (2,4-D) inhalation exposure. We used forty adult male Wistar rats. The rats were divided into four groups: control group (CG), low concentration group (LCG), medium concentration group (MCG), and high concentration group (HCG). In a 6-month exposure period, we used two boxes connected to ultrasonic nebulizers for herbicide spraying. After this period, the rats were euthanized for the collection and study of lung tissue. For each image, counts of injuries and blisters were performed automatically using a methodology based on digital image processing techniques. For analysis of the results, an electronic database (Excel®) was created. We used the Pearson method for correlation analysis; values of p <0.05 were considered significant. In the evaluation of healthy alveoli, we recorded positive and significant correlations between analysis from a pathologist and computational analysis. In the evaluation of injured alveoli, we recorded a positive but non-significant correlation between analysis from a pathologist and computational analysis. These results show the effectiveness of digital image processing when evaluating alveolar integrity.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Ana Paula Alves Favareto
- Environmental and Regional Development, Oeste Paulista University, Presidente Prudente, SP, Brazil
| | - Renata Calciolari Rossi
- Environmental and Regional Development, Oeste Paulista University, Km 572, SP-270 - Bairro Limoeiro, Pres. Prudente, SP, 19026-310, Brazil.
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COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network. Health Inf Sci Syst 2021; 9:36. [PMID: 34659742 PMCID: PMC8509906 DOI: 10.1007/s13755-021-00166-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/20/2021] [Indexed: 12/19/2022] Open
Abstract
Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s13755-021-00166-4.
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Ghaderzadeh M, Aria M, Asadi F. X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9942873. [PMID: 34458373 PMCID: PMC8390162 DOI: 10.1155/2021/9942873] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/16/2021] [Accepted: 08/04/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE Due to the excessive use of raw materials in diagnostic tools and equipment during the COVID-19 pandemic, there is a dire need for cheaper and more effective methods in the healthcare system. With the development of artificial intelligence (AI) methods in medical sciences as low-cost and safer diagnostic methods, researchers have turned their attention to the use of imaging tools with AI that have fewer complications for patients and reduce the consumption of healthcare resources. Despite its limitations, X-ray is suggested as the first-line diagnostic modality for detecting and screening COVID-19 cases. METHOD This systematic review assessed the current state of AI applications and the performance of algorithms in X-ray image analysis. The search strategy yielded 322 results from four databases and google scholar, 60 of which met the inclusion criteria. The performance statistics included the area under the receiver operating characteristics (AUC) curve, accuracy, sensitivity, and specificity. RESULT The average sensitivity and specificity of CXR equipped with AI algorithms for COVID-19 diagnosis were >96% (83%-100%) and 92% (80%-100%), respectively. For common X-ray methods in COVID-19 detection, these values were 0.56 (95% CI 0.51-0.60) and 0.60 (95% CI 0.54-0.65), respectively. AI has substantially improved the diagnostic performance of X-rays in COVID-19. CONCLUSION X-rays equipped with AI can serve as a tool to screen the cases requiring CT scans. The use of this tool does not waste time or impose extra costs, has minimal complications, and can thus decrease or remove unnecessary CT slices and other healthcare resources.
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Affiliation(s)
- Mustafa Ghaderzadeh
- Student Research Committee, Department and Faculty of Health Information Technology and Ma School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrad Aria
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 12/15/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048,] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Correspondence: or
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11157004] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high F1-score of 0.99 on the publicly available SARS-CoV-2 CT dataset. The model proposed in this study is effective for the accurate screening of COVID-19 CT scans and, hence, can be a promising supplementary diagnostic tool for the forefront clinical specialists.
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Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021; 11:1317. [PMID: 34441252 PMCID: PMC8394327 DOI: 10.3390/diagnostics11081317] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 12/23/2022] Open
Abstract
Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy;
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Alessandro Posa
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario Agostino Gemelli—IRCCS, 00168 Rome, Italy;
| | - Pierandrea Cancian
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Sherif Shalaby
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Emanuele Neri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
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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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Ghaderzadeh M, Asadi F, Jafari R, Bashash D, Abolghasemi H, Aria M. Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study. J Med Internet Res 2021; 23:e27468. [PMID: 33848973 PMCID: PMC8078376 DOI: 10.2196/27468] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/26/2021] [Accepted: 04/03/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. OBJECTIVE Machine vision-based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)-based algorithm. METHODS NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. RESULTS After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. CONCLUSIONS The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.
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Affiliation(s)
- Mustafa Ghaderzadeh
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ramezan Jafari
- Department of Radiology, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Davood Bashash
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Abolghasemi
- Pediatric Congenital Hematologic Disorders Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrad Aria
- Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
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Islam MM, Poly TN, Alsinglawi B, Lin LF, Chien SC, Liu JC, Jian WS. Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis. Healthcare (Basel) 2021; 9:441. [PMID: 33918686 PMCID: PMC8070493 DOI: 10.3390/healthcare9040441] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 03/31/2021] [Accepted: 04/08/2021] [Indexed: 01/25/2023] Open
Abstract
The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.
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Affiliation(s)
- Md. Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (S.-C.C.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (S.-C.C.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Belal Alsinglawi
- Department of Bigdata and Mathmatics, Western Sydney University, Penrith, NSW 2751, Australia;
| | - Li-Fong Lin
- School of Gerontology Health Management, College of Nursing, Taipei Medical University, Taipei 110, Taiwan;
- Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, Taipei Medical University, New Taipei 235, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei 110, Taiwan
- Professional Master Program in Medicine, Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 110, Taiwan
| | - Shuo-Chen Chien
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (M.M.I.); (T.N.P.); (S.-C.C.)
| | - Ju-Chi Liu
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University—Shuang Ho Hospital, Taipei 235, Taiwan;
- Research Center for Biomedical Devices Center for Cell Therapy and Regeneration Medicine Taipei Heart Institute (THI), Taipei Medical University, Taipei 110, Taiwan
| | - Wen-Shan Jian
- School of Gerontology Health Management, College of Nursing, Taipei Medical University, Taipei 110, Taiwan;
- Professional Master Program in Medicine, Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 110, Taiwan
- School of Health Care Administration, Taipei Medical University, Taipei 110, Taiwan
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El Asnaoui K. Design ensemble deep learning model for pneumonia disease classification. INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2021; 10:55-68. [PMID: 33643764 PMCID: PMC7896551 DOI: 10.1007/s13735-021-00204-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/06/2021] [Accepted: 01/19/2021] [Indexed: 05/23/2023]
Abstract
With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).
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Affiliation(s)
- Khalid El Asnaoui
- National School of Applied Sciences (ENSA), Department of Computer Sciences, Mohammed First University, BP: 669, 60000 Oujda, Morocco
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