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Khalili H, Wimmer MA. Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic. Life (Basel) 2024; 14:783. [PMID: 39063538 PMCID: PMC11278356 DOI: 10.3390/life14070783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/16/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.
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Affiliation(s)
- Hamed Khalili
- Research Group E-Government, Faculty of Computer Science, University of Koblenz, D-56070 Koblenz, Germany;
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2
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Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Khosravi A, Zare A, Gorriz JM, Chale-Chale AH, Khadem A, Rajendra Acharya U. Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression. Cogn Neurodyn 2023; 17:1501-1523. [PMID: 37974583 PMCID: PMC10640504 DOI: 10.1007/s11571-022-09897-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/23/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.
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Affiliation(s)
- Afshin Shoeibi
- FPGA Lab, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Navid Ghassemi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Juan M. Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
| | | | - Ali Khadem
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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3
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Nizam NB, Siddiquee SM, Shirin M, Bhuiyan MIH, Hasan T. COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model. J Digit Imaging 2023; 36:2100-2112. [PMID: 37369941 PMCID: PMC10502002 DOI: 10.1007/s10278-023-00861-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 05/17/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially chest x-ray and lung Computed Tomography (CT) scans, plays a vital role in the severity analysis of hospitalized COVID-19 patients. However, with an increasing number of patients and a lack of skilled radiologists, automated assessment of COVID-19 severity using medical image analysis has become increasingly important. Chest x-ray (CXR) imaging plays a significant role in assessing the severity of pneumonia, especially in low-resource hospitals, and is the most frequently used diagnostic imaging in the world. Previous methods that automatically predict the severity of COVID-19 pneumonia mainly focus on feature pooling from pre-trained CXR models without explicitly considering the underlying human anatomical attributes. This paper proposes an anatomy-aware (AA) deep learning model that learns the generic features from x-ray images considering the underlying anatomical information. Utilizing a pre-trained model and lung segmentation masks, the model generates a feature vector including disease-level features and lung involvement scores. We have used four different open-source datasets, along with an in-house annotated test set for training and evaluation of the proposed method. The proposed method improves the geographical extent score by 11% in terms of mean squared error (MSE) while preserving the benchmark result in lung opacity score. The results demonstrate the effectiveness of the proposed AA model in COVID-19 severity prediction from chest X-ray images. The algorithm can be used in low-resource setting hospitals for COVID-19 severity prediction, especially where there is a lack of skilled radiologists.
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Affiliation(s)
- Nusrat Binta Nizam
- mHealth Research Group, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh
| | - Sadi Mohammad Siddiquee
- mHealth Research Group, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh
| | - Mahbuba Shirin
- Department of Radiology and Imaging, Bangabandhu Sheikh Mujib Medical University, Shahbagh, Dhaka, 1000, Bangladesh
| | - Mohammed Imamul Hassan Bhuiyan
- Department of Electrical and Electronics Engineering (EEE), Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Taufiq Hasan
- mHealth Research Group, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh.
- Center for Bioengineering Innovation and Design (CBID), Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Moosaei H, Hladík M. Sparse solution of least-squares twin multi-class support vector machine using ℓ 0 and ℓ p-norm for classification and feature selection. Neural Netw 2023; 166:471-486. [PMID: 37574621 DOI: 10.1016/j.neunet.2023.07.039] [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: 11/25/2022] [Revised: 06/22/2023] [Accepted: 07/26/2023] [Indexed: 08/15/2023]
Abstract
In the realm of multi-class classification, the twin K-class support vector classification (Twin-KSVC) generates ternary outputs {-1,0,+1} by evaluating all training data in a "1-versus-1-versus-rest" structure. Recently, inspired by the least-squares version of Twin-KSVC and Twin-KSVC, a new multi-class classifier called improvements on least-squares twin multi-class classification support vector machine (ILSTKSVC) has been proposed. In this method, the concept of structural risk minimization is achieved by incorporating a regularization term in addition to the minimization of empirical risk. Twin-KSVC and its improvements have an influence on classification accuracy. Another aspect influencing classification accuracy is feature selection, which is a critical stage in machine learning, especially when working with high-dimensional datasets. However, most prior studies have not addressed this crucial aspect. In this study, motivated by ILSTKSVC and the cardinality-constrained optimization problem, we propose ℓp-norm least-squares twin multi-class support vector machine (PLSTKSVC) with 0
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Affiliation(s)
- Hossein Moosaei
- Department of Informatics, Faculty of Science, Jan Evangelista Purkyně University, Ústí nad Labem, Czech Republic; Department of Econometrics, Prague University of Economics and Business, Czech Republic.
| | - Milan Hladík
- Department of Applied Mathematics, School of Computer Science, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; Department of Econometrics, Prague University of Economics and Business, Czech Republic.
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5
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Joloudari JH, Azizi F, Nodehi I, Nematollahi MA, Kamrannejhad F, Hassannatajjeloudari E, Alizadehsani R, Islam SMS. Developing a Deep Neural Network model for COVID-19 diagnosis based on CT scan images. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:16236-16258. [PMID: 37920011 DOI: 10.3934/mbe.2023725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.
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Affiliation(s)
| | - Faezeh Azizi
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Issa Nodehi
- Department of Computer Engineering, University of Qom, Qom, Iran
| | | | - Fateme Kamrannejhad
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Edris Hassannatajjeloudari
- Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia
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6
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A comprehensive survey on design and application of autoencoder in deep learning. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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7
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Nematollahi MA, Jahangiri S, Asadollahi A, Salimi M, Dehghan A, Mashayekh M, Roshanzamir M, Gholamabbas G, Alizadehsani R, Bazrafshan M, Bazrafshan H, Bazrafshan Drissi H, Shariful Islam SM. Body composition predicts hypertension using machine learning methods: a cohort study. Sci Rep 2023; 13:6885. [PMID: 37105977 PMCID: PMC10140285 DOI: 10.1038/s41598-023-34127-6] [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: 11/03/2022] [Accepted: 04/25/2023] [Indexed: 04/29/2023] Open
Abstract
We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.
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Affiliation(s)
| | - Soodeh Jahangiri
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Non Communicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Maryam Salimi
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
- Bone and Joint Diseases Research Center, Department of Orthopedic Surgery, Shiraz University of Medical Science, Shiraz, Iran
| | - Azizallah Dehghan
- Non Communicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Mina Mashayekh
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Ghazal Gholamabbas
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | | | - Hanieh Bazrafshan
- Department of Neurology, Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamed Bazrafshan Drissi
- Cardiovascular Research Center, Shiraz University of Medical Sciences, PO Box: 71348-14336, Shiraz, Iran.
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia
- Cardiovascular Division, The George Institute for Global Health, Newtown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
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8
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Yazdani A, Bigdeli SK, Zahmatkeshan M. Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran. Health Sci Rep 2023; 6:e1212. [PMID: 37064314 PMCID: PMC10099201 DOI: 10.1002/hsr2.1212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 04/18/2023] Open
Abstract
Background and Aims Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID-19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID-19 by comparing the accuracy of machine learning (ML) models. Methods It is a cross-sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F-score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion The development of software systems based on NB will be effective to predict the survival of COVID-19 patients.
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Affiliation(s)
- Azita Yazdani
- Department of Health Information Management, School of Health Management and Information SciencesShiraz University of Medical SciencesShirazIran
- Clinical Education Research CenterShiraz University of Medical SciencesShirazIran
- Health Human Resources Research Center, School of Health Management and Information SciencesShiraz University of Medical SciencesShirazIran
| | - Somayeh Kianian Bigdeli
- Health Information Management Department, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
| | - Maryam Zahmatkeshan
- Noncommunicable Diseases Research CenterFasa University of Medical SciencesFasaIran
- School of Allied Medical SciencesFasa University of Medical SciencesFasaIran
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9
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Yadav DP, Jalal AS, Goyal A, Mishra A, Uprety K, Guragai N. COVID-19 radiograph prognosis using a deep CResNeXt network. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-27. [PMID: 37362635 PMCID: PMC9993361 DOI: 10.1007/s11042-023-14960-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/07/2022] [Accepted: 02/22/2023] [Indexed: 06/28/2023]
Abstract
COVID-19 has caused an epidemic in the entire world and it is caused by the novel virus SARS-COV-2. In severe conditions, this virus can cause a critical lung infection or viral pneumonia. To administer the correct treatment to patients, COVID-19 testing is important for diagnosing and determining patients who are infected with COVID-19, as opposed to those infected with other bacterial or viral infections. In this paper, a CResNeXt chest radiograph COVID-19 prediction model is proposed using residual network architecture. The advantage of the proposed model is that it requires lesser free hyper-parameters as compared to other residual networks. In addition, the training time per epochs of the model is very less compared to VGG19, ResNet-50, ResNeXt. The proposed CResNeXt model's binary classification (COVID-19 versus No-Finding) accuracy is observed to be 98.63% and 99.99% and multi-class classification (COVID-19, Pneumonia, and No-Finding) accuracy is observed to be 97.42% and 99.27% on the original and augmented datasets, respectively.
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Affiliation(s)
- Dhirendra P. Yadav
- Department of Computer Engineering & Applications, G.L.A. University, Mathura, UP India
| | - Anand Singh Jalal
- Department of Computer Engineering & Applications, G.L.A. University, Mathura, UP India
| | - Ayush Goyal
- Department of Electrical Engineering and Computer Science, Texas A&M University, Kingsville, TX USA
| | - Avdesh Mishra
- Department of Electrical Engineering and Computer Science, Texas A&M University, Kingsville, TX USA
| | - Khem Uprety
- The University of Tennessee Health Science Center, Memphis, TN USA
| | - Nirmal Guragai
- Department of Cardiology, St. Joseph Regional Medical Center, Paterson, NJ USA
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10
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Identification of Clinical Features Associated with Mortality in COVID-19 Patients. OPERATIONS RESEARCH FORUM 2023. [PMCID: PMC9984757 DOI: 10.1007/s43069-022-00191-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
AbstractUnderstanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings.
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Mahdavi M, Choubdar H, Rostami Z, Niroomand B, Levine AT, Fatemi A, Bolhasani E, Vahabie AH, Lomber SG, Merrikhi Y. Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study. Sci Rep 2023; 13:2827. [PMID: 36808151 PMCID: PMC9936112 DOI: 10.1038/s41598-023-29334-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 02/01/2023] [Indexed: 02/19/2023] Open
Abstract
Medical machine learning frameworks have received much attention in recent years. The recent COVID-19 pandemic was also accompanied by a surge in proposed machine learning algorithms for tasks such as diagnosis and mortality prognosis. Machine learning frameworks can be helpful medical assistants by extracting data patterns that are otherwise hard to detect by humans. Efficient feature engineering and dimensionality reduction are major challenges in most medical machine learning frameworks. Autoencoders are novel unsupervised tools that can perform data-driven dimensionality reduction with minimum prior assumptions. This study, in a novel approach, investigated the predictive power of latent representations obtained from a hybrid autoencoder (HAE) framework combining variational autoencoder (VAE) characteristics with mean squared error (MSE) and triplet loss for forecasting COVID-19 patients with high mortality risk in a retrospective framework. Electronic laboratory and clinical data of 1474 patients were used in the study. Logistic regression with elastic net regularization (EN) and random forest (RF) models were used as final classifiers. Moreover, we also investigated the contribution of utilized features towards latent representations via mutual information analysis. HAE Latent representations model achieved decent performance with an area under ROC curve of 0.921 (±0.027) and 0.910 (±0.036) with EN and RF predictors, respectively, over the hold-out data in comparison with the raw (AUC EN: 0.913 (±0.022); RF: 0.903 (±0.020)) models. The study aims to provide an interpretable feature engineering framework for the medical environment with the potential to integrate imaging data for efficient feature engineering in rapid triage and other clinical predictive models.
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Affiliation(s)
- Mahdi Mahdavi
- grid.411600.2Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran ,grid.14709.3b0000 0004 1936 8649Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6 Canada
| | - Hadi Choubdar
- grid.411600.2Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran ,grid.14709.3b0000 0004 1936 8649Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6 Canada
| | - Zahra Rostami
- grid.411600.2Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Behnaz Niroomand
- grid.411600.2Department of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alexandra T. Levine
- grid.39381.300000 0004 1936 8884Department of Psychology, University of Western Ontario, London, Ontario N6A 3K7 Canada
| | - Alireza Fatemi
- grid.411600.2Department of Internal Medicine, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ehsan Bolhasani
- grid.411750.60000 0001 0454 365XDepartment of Physics, University of Isfahan, Isfahan, 81746-73441 Iran
| | - Abdol-Hossein Vahabie
- grid.46072.370000 0004 0612 7950Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran ,grid.46072.370000 0004 0612 7950Department of Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran ,grid.502999.ePasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
| | - Stephen G. Lomber
- grid.14709.3b0000 0004 1936 8649Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC H3G1Y6 Canada
| | - Yaser Merrikhi
- Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, H3G1Y6, Canada.
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12
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Chen X, Bai Y, Wang P, Luo J. Data augmentation based semi-supervised method to improve COVID-19 CT classification. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:6838-6852. [PMID: 37161130 DOI: 10.3934/mbe.2023294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL.
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Affiliation(s)
- Xiangtao Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Yuting Bai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Peng Wang
- College of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
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Noroozi-Ghaleini E, Shaibani MJ. Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks. Heliyon 2023; 9:e13672. [PMID: 36852029 PMCID: PMC9958458 DOI: 10.1016/j.heliyon.2023.e13672] [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: 11/26/2021] [Revised: 02/07/2023] [Accepted: 02/07/2023] [Indexed: 02/13/2023] Open
Abstract
Since 2019, the coronavirus outbreak has caused many catastrophic events all over the world. At the current time, the massive vaccination has been considered as the most efficient way to fight against the pandemic. This study schemes to explain and model COVID-19 cases by considering the vaccination rate. We utilized an amalgamation of neural network (NN) with two powerful optimization algorithms, i.e., firefly algorithm and artificial bee colony. For validating the models, we employed the COVID-19 datasets regarding the vaccination rate and the total confirmed cases for 51 states since the beginning of vaccination in the US. The numerical experiment indicated that by considering the vaccinated population, the accuracy of NN increases exponentially when compared with the same NN in the absence of the vaccinated population. During the next stage, the NN with vaccinated input data is elected for firefly and bee optimizing. Based upon the firefly optimizing, 93.75% of COVID-19 cases can be explained in all states. According to the bee optimizing, 92.3% of COVID-19 cases is explained since the massive vaccination. Overall, it can be concluded that the massive vaccination is the key predictor of COVID-19 cases on a grand scale.
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Affiliation(s)
- Ebrahim Noroozi-Ghaleini
- Mining Engineering Department, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
- Corresponding author.
| | - Mohammad Javad Shaibani
- Department of Health Management and Economics, School of Public Health, Tehran University of MedicalSciences, Tehran, IranSciences, Tehran, Iran
- Corresponding author.
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14
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Matsumoto T, Walston SL, Walston M, Kabata D, Miki Y, Shiba M, Ueda D. Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs. J Digit Imaging 2023; 36:178-188. [PMID: 35941407 PMCID: PMC9360661 DOI: 10.1007/s10278-022-00691-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/20/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022] Open
Abstract
Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.
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Affiliation(s)
- Toshimasa Matsumoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Shannon Leigh Walston
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Michael Walston
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Daijiro Kabata
- Department of Medical Statistics, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Masatsugu Shiba
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.,Department of Medical Statistics, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Daiju Ueda
- Smart Life Science Lab, Center for Health Science Innovation, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan. .,Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
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15
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Gulakala R, Markert B, Stoffel M. Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107262. [PMID: 36463675 PMCID: PMC9699959 DOI: 10.1016/j.cmpb.2022.107262] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 11/04/2022] [Accepted: 11/22/2022] [Indexed: 05/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Covid-19 infections are spreading around the globe since December 2019. Several diagnostic methods were developed based on biological investigations and the success of each method depends on the accuracy of identifying Covid infections. However, access to diagnostic tools can be limited, depending on geographic region and the diagnosis duration plays an important role in treating Covid-19. Since the virus causes pneumonia, its presence can also be detected using medical imaging by Radiologists. Hospitals with X-ray capabilities are widely distributed all over the world, so a method for diagnosing Covid-19 from chest X-rays would present itself. Studies have shown promising results in automatically detecting Covid-19 from medical images using supervised Artificial neural network (ANN) algorithms. The major drawback of supervised learning algorithms is that they require huge amounts of data to train. Also, the radiology equipment is not computationally efficient for deep neural networks. Therefore, we aim to develop a Generative Adversarial Network (GAN) based image augmentation to optimize the performance of custom, light, Convolutional networks used for the classification of Chest X-rays (CXR). METHODS A Progressively Growing Generative Adversarial Network (PGGAN) is used to generate synthetic and augmented data to supplement the dataset. We propose two novel CNN architectures to perform the Multi-class classification of Covid-19, healthy and pneumonia affected Chest X-rays. Comparisons have been drawn to the state of the art models and transfer learning methods to evaluate the superiority of the networks. All the models are trained using enhanced and augmented X-ray images and are compared based on classification metrics. RESULTS The proposed models had extremely high classification metrics with proposed Architectures having test accuracy of 98.78% and 99.2% respectively while having 40% lesser training parameters than their state of the art counterpart. CONCLUSION In the present study, a method based on artificial intelligence is proposed, leading to a rapid diagnostic tool for Covid infections based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN). The benefit will be a high accuracy of detection with up to 99% hit rate, a rapid diagnosis, and an accessible Covid identification method by chest X-ray images.
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Affiliation(s)
- Rutwik Gulakala
- Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, D-52062 Aachen, Germany
| | - Bernd Markert
- Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, D-52062 Aachen, Germany
| | - Marcus Stoffel
- Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, D-52062 Aachen, Germany.
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16
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Khalili H, Rismani M, Nematollahi MA, Masoudi MS, Asadollahi A, Taheri R, Pourmontaseri H, Valibeygi A, Roshanzamir M, Alizadehsani R, Niakan A, Andishgar A, Islam SMS, Acharya UR. Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci Rep 2023; 13:960. [PMID: 36653412 PMCID: PMC9849475 DOI: 10.1038/s41598-023-28188-w] [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: 08/01/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.
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Affiliation(s)
- Hosseinali Khalili
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maziyar Rismani
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | | | - Mohammad Sadegh Masoudi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Reza Taheri
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Hossein Pourmontaseri
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
- Bitab Knowledge Enterprise, Fasa University of Medical Sciences, Fasa, Iran
| | - Adib Valibeygi
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | - Amin Niakan
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Aref Andishgar
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia
- Cardiovascular Division, The George Institute for Global Health, Newtown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
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17
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Ullah Z, Usman M, Latif S, Gwak J. Densely attention mechanism based network for COVID-19 detection in chest X-rays. Sci Rep 2023; 13:261. [PMID: 36609667 PMCID: PMC9816547 DOI: 10.1038/s41598-022-27266-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Siddique Latif
- Faculty of Health and Computing, University of Southern Queensland, Toowoomba, QLD, 4300, Australia
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of IT. Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, 27469, South Korea.
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18
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Marefat A, Marefat M, Hassannataj Joloudari J, Nematollahi MA, Lashgari R. CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers. Front Public Health 2023; 11:1025746. [PMID: 36923036 PMCID: PMC10009152 DOI: 10.3389/fpubh.2023.1025746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 02/07/2023] [Indexed: 03/03/2023] Open
Abstract
COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.
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Affiliation(s)
- Abdolreza Marefat
- Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahdieh Marefat
- Department of Cellular and Molecular Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | | | - Reza Lashgari
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
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19
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Kadhim YA, Khan MU, Mishra A. Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228999. [PMID: 36433595 PMCID: PMC9692938 DOI: 10.3390/s22228999] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 05/26/2023]
Abstract
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.
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Affiliation(s)
- Yezi Ali Kadhim
- Department of Modeling and Design of Engineering Systems (MODES), Atilim University, Ankara 06830, Turkey
- Department of Electrical and Electronics Engineering, Atilim University, Incek, Ankara 06830, Turkey
| | - Muhammad Umer Khan
- Department of Mechatronics Engineering, Atilim University, Incek, Ankara 06830, Turkey
| | - Alok Mishra
- Department of Software Engineering, Atilim University, Incek, Ankara 06830, Turkey
- Informatics and Digitalization Group, Molde University College—Specialized University in Logistics, 6410 Molde, Norway
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20
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Gulakala R, Markert B, Stoffel M. Generative adversarial network based data augmentation for CNN based detection of Covid-19. Sci Rep 2022; 12:19186. [PMID: 36357530 PMCID: PMC9647771 DOI: 10.1038/s41598-022-23692-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022] Open
Abstract
Covid-19 has been a global concern since 2019, crippling the world economy and health. Biological diagnostic tools have since been developed to identify the virus from bodily fluids and since the virus causes pneumonia, which results in lung inflammation, the presence of the virus can also be detected using medical imaging by expert radiologists. The success of each diagnostic method is measured by the hit rate for identifying Covid infections. However, the access for people to each diagnosis tool can be limited, depending on the geographic region and, since Covid treatment denotes a race against time, the diagnosis duration plays an important role. Hospitals with X-ray opportunities are widely distributed all over the world, so a method investigating lung X-ray images for possible Covid-19 infections would offer itself. Promising results have been achieved in the literature in automatically detecting the virus using medical images like CT scans and X-rays using supervised artificial neural network algorithms. One of the major drawbacks of supervised learning models is that they require enormous amounts of data to train, and generalize on new data. In this study, we develop a Swish activated, Instance and Batch normalized Residual U-Net GAN with dense blocks and skip connections to create synthetic and augmented data for training. The proposed GAN architecture, due to the presence of instance normalization and swish activation, can deal with the randomness of luminosity, that arises due to different sources of X-ray images better than the classical architecture and generate realistic-looking synthetic data. Also, the radiology equipment is not generally computationally efficient. They cannot efficiently run state-of-the-art deep neural networks such as DenseNet and ResNet effectively. Hence, we propose a novel CNN architecture that is 40% lighter and more accurate than state-of-the-art CNN networks. Multi-class classification of the three classes of chest X-rays (CXR), ie Covid-19, healthy and Pneumonia, is performed using the proposed model which had an extremely high test accuracy of 99.2% which has not been achieved in any previous studies in the literature. Based on the mentioned criteria for developing Corona infection diagnosis, in the present study, an Artificial Intelligence based method is proposed, resulting in a rapid diagnostic tool for Covid infections based on generative adversarial and convolutional neural networks. The benefit will be a high accuracy of lung infection identification with 99% accuracy. This could lead to a support tool that helps in rapid diagnosis, and an accessible Covid identification method using CXR images.
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Affiliation(s)
- Rutwik Gulakala
- grid.1957.a0000 0001 0728 696XInstitute of General Mechanics, RWTH Aachen University, Aachen, Germany
| | - Bernd Markert
- grid.1957.a0000 0001 0728 696XInstitute of General Mechanics, RWTH Aachen University, Aachen, Germany
| | - Marcus Stoffel
- grid.1957.a0000 0001 0728 696XInstitute of General Mechanics, RWTH Aachen University, Aachen, Germany
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21
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Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2022. [DOI: 10.2478/pjmpe-2022-0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT.
Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists.
Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy.
Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.
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22
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Kumar S, Shastri S, Mahajan S, Singh K, Gupta S, Rani R, Mohan N, Mansotra V. LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1464-1480. [PMID: 35941931 PMCID: PMC9349394 DOI: 10.1002/ima.22770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 02/26/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy.
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Affiliation(s)
- Sachin Kumar
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
| | - Sourabh Shastri
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
| | - Shilpa Mahajan
- Department of Computer Science and EngineeringNational Institute of TechnologyJalandharIndia
| | - Kuljeet Singh
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
| | - Surbhi Gupta
- Department of Electrical Engineering and Information TechnologyPunjab Agricultural UniversityLudhianaIndia
| | - Rajneesh Rani
- Department of Computer Science and EngineeringNational Institute of TechnologyJalandharIndia
| | - Neeraj Mohan
- Department of Computer Science and EngineeringIK Gujral Punjab Technical UniversityMohaliIndia
| | - Vibhakar Mansotra
- Department of Computer Science and ITUniversity of JammuJammu and KashmirIndia
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23
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Banerjee A, Sarkar A, Roy S, Singh PK, Sarkar R. COVID-19 chest X-ray detection through blending ensemble of CNN snapshots. Biomed Signal Process Control 2022; 78:104000. [PMID: 35855489 PMCID: PMC9283670 DOI: 10.1016/j.bspc.2022.104000] [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: 03/30/2022] [Revised: 06/23/2022] [Accepted: 07/11/2022] [Indexed: 12/04/2022]
Abstract
The novel COVID-19 pandemic, has effectively turned out to be one of the deadliest events in modern history, with unprecedented loss of human life, major economic and financial setbacks and has set the entire world back quite a few decades. However, detection of the COVID-19 virus has become increasingly difficult due to the mutating nature of the virus, and the rise in asymptomatic cases. To counteract this and contribute to the research efforts for a more accurate screening of COVID-19, we have planned this work. Here, we have proposed an ensemble methodology for deep learning models to solve the task of COVID-19 detection from chest X-rays (CXRs) to assist Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of transfer learning for Convolutional Neural Networks (CNNs), widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The DenseNet-201 architecture has been trained only once to generate multiple snapshots, offering diverse information about the extracted features from CXRs. We follow the strategy of decision-level fusion to combine the decision scores using the blending algorithm through a Random Forest (RF) meta-learner. Experimental results confirm the efficacy of the proposed ensemble method, as shown through impressive results upon two open access COVID-19 CXR datasets - the largest COVID-X dataset, as well as a smaller scale dataset. On the large COVID-X dataset, the proposed model has achieved an accuracy score of 94.55% and on the smaller dataset by Chowdhury et al., the proposed model has achieved a 98.13% accuracy score.
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Affiliation(s)
- Avinandan Banerjee
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India
| | - Arya Sarkar
- Department of Computer Science, University of Engineering and Management, University Area, Plot No. III - B/5, New Town, Action Area - III, Kolkata 700160, West Bengal, India
| | - Sayantan Roy
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, West Bengal, India
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Hamza A, Attique Khan M, Wang SH, Alqahtani A, Alsubai S, Binbusayyis A, Hussein HS, Martinetz TM, Alshazly H. COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization. Front Public Health 2022; 10:948205. [PMID: 36111186 PMCID: PMC9468600 DOI: 10.3389/fpubh.2022.948205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/01/2022] [Indexed: 01/21/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.
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Affiliation(s)
- Ameer Hamza
- Department of Computer Science, HITEC University, Taxila, Pakistan
| | - Muhammad Attique Khan
- Department of Computer Science, HITEC University, Taxila, Pakistan,*Correspondence: Muhammad Attique Khan
| | - Shui-Hua Wang
- Department of Mathematics, University of Leicester, Leicester, United Kingdom
| | - Abdullah Alqahtani
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Shtwai Alsubai
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Adel Binbusayyis
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Hany S. Hussein
- Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia,Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt
| | | | - Hammam Alshazly
- Faculty of Computers and Information, South Valley University, Qena, Egypt
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25
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Jain P, Sahu S. Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2022; 34:e7286. [PMID: 36247093 PMCID: PMC9539277 DOI: 10.1002/cpe.7286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 06/07/2022] [Accepted: 07/15/2022] [Indexed: 06/16/2023]
Abstract
How will the newly discovered coronavirus (COVID-19) affect the world and what will be its global impact? For answering this question, we will require a prediction of overall recoveries and fatalities, as well as a reliable prognosis of coronavirus cases. Predicting, however, requires an ample total of past data related to it. On any particular day, the prediction is unclear since events in the future rarely repeat themselves the way that they did in the past. Furthermore, forecasts and predictions are determined by the absolute interests, accuracy of the data, and prophesied variables. In addition, psychological factors play an enormous role in how people perceive and react to the danger from the disease and therefore the fear that it is going to affect them personally. This research paper advances an unbiased method for predicting the increase of the COVID-19 employing a simple, but powerful method to do so. Assumed that the data are accurate and reliable which the longer term will still follow an equivalent disease pattern, our projections intimate with a large association. Within the COVID-19 cases were documented, in contingency, there is a steady increase. The hazards are far away from symmetric, as underestimating a pandemic's spread and failing to do enough to prevent it is far a lot worse than overspending and being too cautious when it will not be needed. This paper illustrates the timeline of a live forecasting study with huge implied implications for devising and decision-making and gives unbiased predictions on COVID-19 confirmed cases, recovered cases, deaths, and ongoing cases are shown on a continental map using data science and machine learning (ML) approaches. Utilizing these ML-based techniques, the proposed system predicts the accurate COVID-19 cases and gives better performance.
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Affiliation(s)
- Priyank Jain
- Indian Institute of Information TechnologyBhopalMadhya PradeshIndia
| | - Shriya Sahu
- Atal Bihari Vajpayee UniversityBilaspurChhattisgarhIndia
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26
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Healthcare providers’ perspectives on using smart home systems to improve self-management and care in people with heart failure: A qualitative study. Int J Med Inform 2022; 167:104837. [DOI: 10.1016/j.ijmedinf.2022.104837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/24/2022] [Accepted: 07/19/2022] [Indexed: 11/19/2022]
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Gomes R, Kamrowski C, Langlois J, Rozario P, Dircks I, Grottodden K, Martinez M, Tee WZ, Sargeant K, LaFleur C, Haley M. A Comprehensive Review of Machine Learning Used to Combat COVID-19. Diagnostics (Basel) 2022; 12:1853. [PMID: 36010204 PMCID: PMC9406981 DOI: 10.3390/diagnostics12081853] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 12/19/2022] Open
Abstract
Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.
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Affiliation(s)
- Rahul Gomes
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Connor Kamrowski
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Jordan Langlois
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Papia Rozario
- Department of Geography and Anthropology, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA;
| | - Ian Dircks
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Keegan Grottodden
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Matthew Martinez
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Wei Zhong Tee
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Kyle Sargeant
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Corbin LaFleur
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Mitchell Haley
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
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Elshennawy NM, Ibrahim DM, Sarhan AM, Arafa M. Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19. Diagnostics (Basel) 2022; 12:1847. [PMID: 36010198 PMCID: PMC9406405 DOI: 10.3390/diagnostics12081847] [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: 06/10/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 11/16/2022] Open
Abstract
The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.
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Affiliation(s)
- Nada M. Elshennawy
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Dina M. Ibrahim
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Amany M. Sarhan
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Mohamed Arafa
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
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29
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Dasgupta A, Bakshi A, Mukherjee S, Das K, Talukdar S, Chatterjee P, Mondal S, Das P, Ghosh S, Som A, Roy P, Kundu R, Sarkar A, Biswas A, Paul K, Basak S, Manna K, Saha C, Mukhopadhyay S, Bhattacharyya NP, De RK. Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2022; 12:e1462. [PMID: 35942397 PMCID: PMC9350133 DOI: 10.1002/widm.1462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 03/28/2022] [Accepted: 04/28/2022] [Indexed: 05/02/2023]
Abstract
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.
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Affiliation(s)
- Abhijit Dasgupta
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Abhisek Bakshi
- Department of Information TechnologyBengal Institute of TechnologyKolkataWest BengalIndia
| | - Srijani Mukherjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Kuntal Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Soumyajeet Talukdar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pratyayee Chatterjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Sagnik Mondal
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Puspita Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Subhrojit Ghosh
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Archisman Som
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pritha Roy
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Rima Kundu
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Akash Sarkar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Arnab Biswas
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Karnelia Paul
- Department of BiotechnologyUniversity of CalcuttaKolkataWest BengalIndia
| | - Sujit Basak
- Department of Physiology and BiophysicsStony Brook UniversityStony BrookNew YorkUSA
| | - Krishnendu Manna
- Department of Food and NutritionUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Chinmay Saha
- Department of Genome Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Satinath Mukhopadhyay
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Nitai P. Bhattacharyya
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Rajat K. De
- Machine Intelligence UnitIndian Statistical InstituteKolkataWest BengalIndia
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30
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Mannepalli DP, Namdeo V. An effective detection of COVID-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:1049-1067. [PMID: 35937036 PMCID: PMC9347606 DOI: 10.1002/ima.22747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 05/08/2023]
Abstract
COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.
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Affiliation(s)
- Durga Prasad Mannepalli
- Department of Computer Science and EngineeringSarvepalli Radhakrishna UniversityBhopalMadhya PradeshIndia
| | - Varsha Namdeo
- Department of Computer Science and EngineeringSarvepalli Radhakrishna UniversityBhopalMadhya PradeshIndia
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31
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Abdalrada AS, Abawajy J, Al-Quraishi T, Islam SMS. Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study. J Diabetes Metab Disord 2022; 21:251-261. [PMID: 35673486 PMCID: PMC9167176 DOI: 10.1007/s40200-021-00968-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 12/29/2021] [Indexed: 12/15/2022]
Abstract
Background Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. Methods We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. Results Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. Conclusions Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.
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Affiliation(s)
- Ahmad Shaker Abdalrada
- Faculty of Computer Science and Information Technology, Wasit University, Al Kut, Iraq
- School of Information Technology, Deakin University, Melbourne, Victoria Australia
| | - Jemal Abawajy
- School of Information Technology, Deakin University, Melbourne, Victoria Australia
| | - Tahsien Al-Quraishi
- Faculty of Computer Science and Information Technology, Wasit University, Al Kut, Iraq
- School of Information Technology, Deakin University, Melbourne, Victoria Australia
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, Deakin University, 221 Burwood Highway, Burwood, Melbourne, VIC 3125 Australia
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32
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Li Y, Zhao H, Gan T, Liu Y, Zou L, Xu T, Chen X, Fan C, Wu M. Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform. Front Public Health 2022; 10:886958. [PMID: 35692335 PMCID: PMC9174692 DOI: 10.3389/fpubh.2022.886958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.
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Affiliation(s)
- Yanhan Li
- Electronic Information School, Wuhan University, Wuhan, China
| | - Hongyun Zhao
- Department of Gastroenterology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Ultrasound Molecular Imaging, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tian Gan
- Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yang Liu
- School of Economics and Management, Wuhan University, Wuhan, China
| | - Lian Zou
- Electronic Information School, Wuhan University, Wuhan, China
| | - Ting Xu
- Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xuan Chen
- Beijing Genomics Institute (BGI) Research, Shenzhen, China
| | - Cien Fan
- Electronic Information School, Wuhan University, Wuhan, China
- *Correspondence: Cien Fan
| | - Meng Wu
- Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China
- Meng Wu
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Karpiński R, Krakowski P, Jonak J, Machrowska A, Maciejewski M, Nogalski A. Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint. SENSORS (BASEL, SWITZERLAND) 2022; 22:3765. [PMID: 35632174 PMCID: PMC9146478 DOI: 10.3390/s22103765] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/10/2022] [Accepted: 05/15/2022] [Indexed: 12/04/2022]
Abstract
Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses.
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Affiliation(s)
- Robert Karpiński
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland; (J.J.); (A.M.)
| | - Przemysław Krakowski
- Department of Trauma Surgery and Emergency Medicine, Medical University of Lublin, Staszica 11, 20-081 Lublin, Poland;
- Orthopaedic Department, Łęczna Hospital, Krasnystawska 52, 21-010 Łęczna, Poland
| | - Józef Jonak
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland; (J.J.); (A.M.)
| | - Anna Machrowska
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland; (J.J.); (A.M.)
| | - Marcin Maciejewski
- Department of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland;
| | - Adam Nogalski
- Department of Trauma Surgery and Emergency Medicine, Medical University of Lublin, Staszica 11, 20-081 Lublin, Poland;
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Kabir HMD, Khanam S, Khozeimeh F, Khosravi A, Mondal SK, Nahavandi S, Acharya UR. Aleatory-aware deep uncertainty quantification for transfer learning. Comput Biol Med 2022; 143:105246. [PMID: 35131610 DOI: 10.1016/j.compbiomed.2022.105246] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/30/2021] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
Abstract
The user does not have any idea about the credibility of outcomes from deep neural networks (DNN) when uncertainty quantification (UQ) is not employed. However, current Deep UQ classification models capture mostly epistemic uncertainty. Therefore, this paper aims to propose an aleatory-aware Deep UQ method for classification problems. First, we train DNNs through transfer learning and collect numeric output posteriors for all training samples instead of logical outputs. Then we determine the probability of happening a certain class from K-nearest output posteriors of the same DNN in training samples. We name this probability as opacity score, as the paper focuses on the detection of opacity on X-ray images. This score reflects the level of aleatory on the sample. When the NN is certain on the classification of the sample, the probability of happening a class becomes much higher than the probabilities of others. Probabilities for different classes become close to each other for a highly uncertain classification outcome. To capture the epistemic uncertainty, we train multiple DNNs with different random initializations, model selection, and augmentations to observe the effect of these training parameters on prediction and uncertainty. To reduce execution time, we first obtain features from the pre-trained NN. Then we apply features to the ensemble of fully connected layers to get the distribution of opacity score during the test. We also train several ResNet and DenseNet DNNs to observe the effect of model selection on prediction and uncertainty. The paper also demonstrates a patient referral framework based on the proposed uncertainty quantification. The scripts of the proposed method are available at the following link: https://github.com/dipuk0506/Aleatory-aware-UQ.
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Affiliation(s)
- H M Dipu Kabir
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia.
| | | | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Subrota Kumar Mondal
- Faculty of Information Technology, Macau University of Science and Technology, Macao
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia; Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, 02 134, USA
| | - U Rajendra Acharya
- Department of ECE, Ngee Ann Polytechnic, 535 Clementi Road, 599 489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Alizadehsani R, Eskandarian R, Behjati M, Zahmatkesh M, Roshanzamir M, Izadi NH, Shoeibi A, Haddadi A, Khozeimeh F, Sani FA, Sani ZA, Roshanzamir Z, Khosravi A, Nahavandi S, Sarrafzadegan N, Islam SMS. Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID-19. Immun Inflamm Dis 2022; 10:e561. [PMID: 35048534 PMCID: PMC8926510 DOI: 10.1002/iid3.561] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 10/25/2021] [Accepted: 10/28/2021] [Indexed: 12/01/2022] Open
Abstract
INTRODUCTION To reduce mortality in hospitalized patients with COVID-19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID-19. METHODS This study was conducted on 660 hospitalized patients with CVD and COVID-19 recruited between January 2020 and January 2021 in Iran. All patients were diagnosed with the previous history of CVD like angina, myocardial infarction, heart failure, cardiomyopathy, abnormal heart rhythms, and congenital heart disease before they were hospitalized for COVID-19. We collected data on patient's signs and symptoms, clinical and paraclinical examinations, and any underlying comorbidities. t test was used to determine the significant difference between the two deceased and alive groups. In addition, the relation between pairs of symptoms and pairs of comorbidities has been determined via correlation computation. RESULTS Our findings suggest that signs and symptoms such as fever, cough, myalgia, chest pain, chills, abdominal pain, nausea, vomiting, diarrhea, and anorexia had no impact on patients' mortality. There was a significant correlation between COVID-19 cardiovascular patients' mortality rate and symptoms such as headache, loss of consciousness (LOC), oxygen saturation less than 93%, and need for mechanical ventilation. CONCLUSIONS Our results might help physicians identify early symptoms, comorbidities, and risk factors related to mortality in CVD patients hospitalized for COVID-19.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI)Deakin UniversityGeelongAustralia
| | - Rahimeh Eskandarian
- Internal Medicine Research CenterSemnan University of Medical SciencesSemnanIran
| | - Mohaddeseh Behjati
- Rajaei Cardiovascular Medical and Research CenterIran University of Medical SciencesTehranIran
| | - Mehrdad Zahmatkesh
- Internal Medicine Research CenterSemnan University of Medical SciencesSemnanIran
| | | | - Navid H. Izadi
- Department of Electrical and Computer EngineeringIsfahan University of TechnologyIsfahanIran
| | - Afshin Shoeibi
- Computer Engineering DepartmentFerdowsi University of MashhadMashhadIran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition LabK. N. Toosi University of TechnologyTehranIran
| | - Azadeh Haddadi
- Department of Biology, Faculy of Basic Sciences, Shahrekord BranchIslamic Azad UniversityShahrekordIran
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI)Deakin UniversityGeelongAustralia
| | - Fariba A. Sani
- Faculty of MedicineMashhad University of Medical ScienceMashhadIran
| | | | - Zahra Roshanzamir
- Pediatric Respiratory and Sleep Medicine Research Center, Children's Medical CenterTehran University of Medical SciencesTehranIran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI)Deakin UniversityGeelongAustralia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI)Deakin UniversityGeelongAustralia
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research InstituteIsfahan University of Medical SciencesIsfahanIran
- Faculty of Medicine, SPPHUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition SciencesDeakin UniversityGeelongVictoriaAustralia
- Cardiovascular DivisionThe George Institute for Global HealthNewtownAustralia
- Sydney Medical SchoolUniversity of SydneyCamperdownAustralia
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Nahavandi D, Alizadehsani R, Khosravi A, Acharya UR. Application of artificial intelligence in wearable devices: Opportunities and challenges. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106541. [PMID: 34837860 DOI: 10.1016/j.cmpb.2021.106541] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/07/2021] [Accepted: 11/15/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data. METHODS Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives. RESULTS Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays. CONCLUSION The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.
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Affiliation(s)
- Darius Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
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Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5040078] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.
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Malekzadeh A, Zare A, Yaghoobi M, Kobravi HR, Alizadehsani R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. SENSORS (BASEL, SWITZERLAND) 2021; 21:7710. [PMID: 34833780 PMCID: PMC8624422 DOI: 10.3390/s21227710] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/15/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022]
Abstract
Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5-40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN-RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN-RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN-RNN classification procedure. The results revealed that the proposed CNN-RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.
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Affiliation(s)
- Anis Malekzadeh
- Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Assef Zare
- Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad 6518115743, Iran;
| | - Mahdi Yaghoobi
- Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran; (M.Y.); (H.-R.K.)
| | - Hamid-Reza Kobravi
- Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran; (M.Y.); (H.-R.K.)
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia;
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Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P, Khadem A, Alizadehsani R, Zare A, Kong Y, Khosravi A, Nahavandi S, Hussain S, Acharya UR, Berk M. Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review. Comput Biol Med 2021; 139:104949. [PMID: 34737139 DOI: 10.1016/j.compbiomed.2021.104949] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/02/2021] [Accepted: 10/13/2021] [Indexed: 01/23/2023]
Abstract
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.
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Affiliation(s)
- Marjane Khodatars
- Dept. of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran; Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - Delaram Sadeghi
- Dept. of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Navid Ghaasemi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran; Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Yinan Kong
- School of Engineering, Macquarie University, Sydney, 2109, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | | | - U Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489, Singapore; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; Dept. of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia
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