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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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Huang L, Ye C, Zhou R, Ji Z. Diagnostic value of routine blood tests in differentiating between SARS-CoV-2, influenza A, and RSV infections in hospitalized children: a retrospective study. BMC Pediatr 2024; 24:328. [PMID: 38741033 DOI: 10.1186/s12887-024-04822-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), influenza A, and respiratory syncytial virus (RSV) infections have similar modes of transmission and clinical symptoms. There is a need to identify simple diagnostic indicators to distinguish these three infections, particularly for community hospitals and low- and middle-income countries that lack nucleic acid detection kits. This study used clinical data to assess the diagnostic value of routine blood tests in differentiating between SARS-CoV-2, influenza A, and RSV infections in children. METHODS A total of 1420 children treated at the Hangzhou Children's Hospital between December 2022 and June 2023 were enrolled in this study, of whom 351 had SARS-CoV-2, 671 had influenza, and 398 had RSV. In addition, 243 healthy children were also collected. The blood test results of SARS-CoV-2 patients were compared to those of patients with influenza A and RSV and the healthy controls. The area under the receiver operating characteristic curve (AUC-ROC) was employed to evaluate each blood parameter's diagnostic value. RESULTS Children with SARS-CoV-2 exhibited notably elevated levels of white blood cell (WBC) count, platelet (PLT) count, neutrophil count, and neutrophil-to-lymphocyte ratio (NLR) compared to influenza A patients (P < 0.05). In contrast, SARS-CoV-2 patients exhibited a decrease in the mean platelet volume to platelet count ratio (MPV/PLT) and the lymphocyte-to-monocyte ratio (LMR) when compared to other individuals (P < 0.05). These parameters had an AUC between 0.5 and 0.7. Compared to patients with RSV, SARS-CoV-2 patients had significantly higher MPV/PLT and significantly lower WBC, lymphocyte, PLT, LMR, and lymphocyte multiplied by platelet (LYM*PLT) values (P < 0.05). However, only LYM*PLT had an acceptable diagnostic value above 0.7 for all age groups. Compared to healthy children, children with COVID-19 exhibited elevated NLR and MPV/PLT levels, alongside decreased lymphocyte, PLT, LMR, and LYM*PLT values. (P < 0.05). The AUC of the LMR, LYM*PLT, and PLT were above 0.7 in all age groups, indicating promising diagnostic values. CONCLUSIONS The routine blood parameters among patients with COVID-19, influenza A, and RSV differ significantly early in the disease and could be used by clinicians to discriminate between the 3 types of infection.
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Affiliation(s)
- Longli Huang
- Hangzhou Children's Hospital, 201 Wenhui Rd, Hangzhou, Zhejiang, China
| | - Cuiying Ye
- Hangzhou Children's Hospital, 201 Wenhui Rd, Hangzhou, Zhejiang, China
| | - Renxi Zhou
- Hangzhou Children's Hospital, 201 Wenhui Rd, Hangzhou, Zhejiang, China
| | - Zexuan Ji
- Hangzhou Children's Hospital, 201 Wenhui Rd, Hangzhou, Zhejiang, China.
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Zabeeda W, Cohen JB, Reiner Benaim A, Zarour S, Lichter Y, Matot I, Goren O. Utility of NICaS Non-Invasive Hemodynamic Monitoring in Critically Ill Patients with COVID-19. J Clin Med 2024; 13:2072. [PMID: 38610837 PMCID: PMC11012855 DOI: 10.3390/jcm13072072] [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: 02/17/2024] [Revised: 03/21/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
(1) Background: COVID-19 presented many challenges to effective treatments, such as managing cardiovascular insufficiency while mitigating risks to healthcare providers. This study utilized NICaS, a non-invasive hemodynamic monitor that provides advanced data via whole-body impedance analysis. We investigated the associated trends in hemodynamic parameters obtained by the NICaS device and their correlation with in-hospital all-cause mortality during COVID-19 hospitalization in the intensive care unit. (2) Methods: Data from 29 patients with COVID-19 admitted to the intensive care unit and monitored with NICaS between April 2020 and February 2021 were analyzed retrospectively. (3) Results: Decreasing cardiac output and cardiac power were significantly associated with death. Total peripheral resistance was significantly increasing in non-survivors as was total body water percentage. Those admitted with a heart rate above 90 beats per minute had a significantly reduced survival. (4) Conclusions: Non-invasive hemodynamic monitoring via the NICaS device is simple and effective in evaluating critically ill patients with COVID-19 and may help guide clinical management via remote monitoring. Controlling tachycardia may help ensure adequate oxygen supply-demand ratio. A hint toward a beneficiary effect of a restrictive fluid balance may be observed.
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Affiliation(s)
- Wisam Zabeeda
- Department of Anesthesiology, Pain and Intensive Care, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 6997801, Israel; (W.Z.); (S.Z.); (Y.L.); (I.M.); (O.G.)
| | - Jonah Benjamin Cohen
- Department of Anesthesiology, Pain and Intensive Care, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 6997801, Israel; (W.Z.); (S.Z.); (Y.L.); (I.M.); (O.G.)
| | - Anat Reiner Benaim
- Department of Epidemiology, Biostatistics and Community Health Sciences, School of Public Health, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva 8410501, Israel;
| | - Shiri Zarour
- Department of Anesthesiology, Pain and Intensive Care, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 6997801, Israel; (W.Z.); (S.Z.); (Y.L.); (I.M.); (O.G.)
| | - Yael Lichter
- Department of Anesthesiology, Pain and Intensive Care, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 6997801, Israel; (W.Z.); (S.Z.); (Y.L.); (I.M.); (O.G.)
| | - Idit Matot
- Department of Anesthesiology, Pain and Intensive Care, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 6997801, Israel; (W.Z.); (S.Z.); (Y.L.); (I.M.); (O.G.)
| | - Or Goren
- Department of Anesthesiology, Pain and Intensive Care, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv 6997801, Israel; (W.Z.); (S.Z.); (Y.L.); (I.M.); (O.G.)
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Iam-Arunthai K, Chamnanchanunt S, Thungthong P, Intalapaporn P, Nakhahes C, Suwanban T, Rojnuckarin P. Thrombosis and Bleeding Risk Scores Are Strongly Associated with Mortality in Hospitalized Patients with COVID-19: A Multicenter Cohort Study. J Clin Med 2024; 13:1437. [PMID: 38592277 PMCID: PMC10932358 DOI: 10.3390/jcm13051437] [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: 02/05/2024] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Internationally established guidelines mention pharmacological prophylaxis for all hospitalized COVID-19 patients. However, there are concerns regarding the efficacy and safety of anticoagulants. This study investigated the associations between thrombosis/bleeding risk scores and clinical outcomes. Methods: We conducted a retrospective review of adult patients admitted to two hospitals between 2021 and 2022. We analyzed clinical data, laboratory results, low molecular weight heparin (LMWH) use, thrombosis, bleeding, and 30-day survival. Results: Of the 160 patients, 69.4% were female, and the median age was 59 years. The rates of thrombotic complications and mortality were 12.5% and 36.3%, respectively. LMWH prophylaxis was administered to 73 of the patients (45.6%). The patients with high Padua prediction scores (PPS) and high IMPROVEVTE scores had a significantly higher risk of venous thromboembolism (VTE) compared to those with low scores (30.8% vs. 9.0%, p = 0.006 and 25.6% vs. 7.7%, p = 0.006). Similarly, elevated IMPROVEVTE and IMPROVEBRS scores were associated with increased mortality (hazard ratios of 7.49 and 6.27, respectively; p < 0.001). Interestingly, LMWH use was not associated with a decreased incidence of VTE when stratified by risk groups. Conclusions: this study suggests that COVID-19 patients with high thrombosis and bleeding risk scores have a higher mortality rate.
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Affiliation(s)
- Kunapa Iam-Arunthai
- Division of Hematology, Department of Medicine, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok 10400, Thailand
| | - Supat Chamnanchanunt
- Division of Hematology, Department of Medicine, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok 10400, Thailand
- Department of Clinical Tropical Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
| | - Pravinwan Thungthong
- Division of Hematology, Department of Medicine, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok 10400, Thailand
| | - Poj Intalapaporn
- Division of Infectious Diseases, Department of Medicine, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok 10400, Thailand
| | - Chajchawan Nakhahes
- Division of Hematology, Department of Medicine, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok 10400, Thailand
| | - Tawatchai Suwanban
- Division of Hematology, Department of Medicine, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok 10400, Thailand
| | - Ponlapat Rojnuckarin
- Center of Excellence in Translational Hematology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
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Ma Y, Zhang B, Liu Z, Liu Y, Wang J, Li X, Feng F, Ni Y, Li S. IAS-FET: An intelligent assistant system and an online platform for enhancing successful rate of in-vitro fertilization embryo transfer technology based on clinical features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108050. [PMID: 38301430 DOI: 10.1016/j.cmpb.2024.108050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Among all of the assisted reproductive technology (ART) methods, in vitro fertilization-embryo transfer (IVF-ET) holds a prominent position as a key solution for overcoming infertility. However, its success rate hovers at a modest 30% to 70%. Adding to the challenge is the absence of effective models and clinical tools capable of predicting the outcome of IVF-ET before embryo formation. Our study is dedicated to filling this critical gap by aiming to predict IVF-ET outcomes and ultimately enhance the success rate of this transformative procedure. METHODS In this retrospective study, infertile patients who received artificial assisted pregnancy treatment at Gansu Provincial Maternity and Child-care Hospital in China were enrolled from 2016 to 2020. Individual's clinical information were studied by cascade XGBoost method to build an intelligent assisted system for predicting the outcome of IVF-ET, called IAS-FET. The cascade XGBoost model was trained using clinical information from 2292 couples and externally tested using clinical information from 573 couples. In addition, several schemes which will be of help for patients to adjust their physical condition to improve their success rate on ART were suggested by IAS-FET. RESULTS The outcome of IVF-ET can be predicted by the built IAS-FET method with the area under curve (AUC) value of 0.8759 on the external test set. Besides, this IAS-FET method can provide several schemes to improve the successful rate of IVF-ET outcomes. The built tool for IAS-FET is addressed as a free platform online at http://www.cppdd.cn/ART for the convenient usage of users. CONCLUSIONS It suggested the significant influence of personal clinical features for the success of ART. The proposed system IAS-FET based on the top 27 factors could be a promising tool to predict the outcome of ART and propose a plan for the patient's physical adjustment. With the help of IAS-FET, patients can take informed steps towards increasing their chances of a successful outcome on their journey to parenthood.
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Affiliation(s)
- Ying Ma
- Gansu Provincial Maternity and Child-care Hospital, Lanzhou, Gansu 730030, China
| | - Bowen Zhang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei 430073, China
| | - Zhaoqing Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yujie Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Jiarui Wang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Xingxuan Li
- School of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, Gansu 730030, China
| | - Fan Feng
- Gansu Provincial Maternity and Child-care Hospital, Lanzhou, Gansu 730030, China
| | - Yali Ni
- Gansu Provincial Maternity and Child-care Hospital, Lanzhou, Gansu 730030, China
| | - Shuyan Li
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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Ashique S, Mishra N, Mohanto S, Garg A, Taghizadeh-Hesary F, Gowda BJ, Chellappan DK. Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects. Heliyon 2024; 10:e25754. [PMID: 38370192 PMCID: PMC10869876 DOI: 10.1016/j.heliyon.2024.e25754] [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: 08/12/2023] [Revised: 01/25/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.
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Affiliation(s)
- Sumel Ashique
- Department of Pharmaceutical Sciences, Bengal College of Pharmaceutical Sciences & Research, Durgapur, 713212, West Bengal, India
| | - Neeraj Mishra
- Department of Pharmaceutics, Amity Institute of Pharmacy, Amity University, Gwalior, 474005, Madhya Pradesh, India
| | - Sourav Mohanto
- Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India
| | - Ashish Garg
- Guru Ramdas Khalsa Institute of Science and Technology, Pharmacy, Jabalpur, M.P, 483001, India
| | - Farzad Taghizadeh-Hesary
- ENT and Head and Neck Research Center and Department, The Five Senses Health Institute, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Clinical Oncology Department, Iran University of Medical Sciences, Tehran, Iran
| | - B.H. Jaswanth Gowda
- Department of Pharmaceutics, Yenepoya Pharmacy College & Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, 575018, India
- School of Pharmacy, Queen's University Belfast, Medical Biology Centre, Belfast, BT9 7BL, UK
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Bukit Jalil, Kuala Lumpur, 57000, Malaysia
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Zhunissova U, Dzierżak R, Omiotek Z, Lytvynenko V. A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI). J Clin Med 2023; 12:6912. [PMID: 37959377 PMCID: PMC10649663 DOI: 10.3390/jcm12216912] [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: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
The aim of the study was to develop a computerized method for distinguishing COVID-19-affected cases from cases of pneumonia. This task continues to be a real challenge in the practice of diagnosing COVID-19 disease. In the study, a new approach was proposed, using a comprehensive set of diagnostic information (CSDI) including, among other things, medical history, demographic data, signs and symptoms of the disease, and laboratory results. These data have the advantage of being much more reliable compared with data based on a single source of information, such as radiological imaging. On this basis, a comprehensive process of building predictive models was carried out, including such steps as data preprocessing, feature selection, training, and evaluation of classification models. During the study, 9 different methods for feature selection were used, while the grid search method and 12 popular classification algorithms were employed to build classification models. The most effective model achieved a classification accuracy (ACC) of 85%, a sensitivity (TPR) equal to 83%, and a specificity (TNR) of 88%. The model was built using the random forest method with 15 features selected using the recursive feature elimination selection method. The results provide an opportunity to build a computer system to assist the physician in the diagnosis of the COVID-19 disease.
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Affiliation(s)
- Ulzhalgas Zhunissova
- Department of Biostatistics, Bioinformatics and Information Technologies, Astana Medical University, Beibitshilik Street 49A, Astana 010000, Kazakhstan
| | - Róża Dzierżak
- Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38 A, 20-618 Lublin, Poland
| | - Zbigniew Omiotek
- Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38 A, 20-618 Lublin, Poland
| | - Volodymyr Lytvynenko
- Department of Informatics and Computer Science, Kherson National Technical University, Beryslavs’ke Hwy, 24, 730082 Kherson, Kherson Oblast, Ukraine
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Vasei M, Jafari E, Falah Azad V, Safavi M, Sotoudeh M. Molecular Diagnosis of COVID-19; Biosafety and Pre-analytical Recommendations. IRANIAN JOURNAL OF PATHOLOGY 2023; 18:244-256. [PMID: 37942195 PMCID: PMC10628373 DOI: 10.30699/ijp.2023.1988405.3061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/08/2023] [Indexed: 11/10/2023]
Abstract
From the beginning of the COVID-19 epidemic, clinical laboratories around the world have been involved with tests for detection of SARS-CoV-2. At present, RT-PCR (real-time reverse transcription polymerase chain reaction assay) is seen as the gold standard for identifying the virus. Many factors are involved in achieving the highest accuracy in this test, including parameters related to the pre-analysis stage. Having instructions on the type of sample, how to take the sample, and its storage and transportation help control the interfering factors at this stage. Studies have shown that pre-analytical factors might be the cause of the high SARS-CoV-2 test false-negative rates. Also, the safety of personnel in molecular laboratories is of utmost importance, and it requires strict guidelines to ensure the safety of exposed individuals and prevent the virus from spreading. Since the onset of the outbreak, various instructions and guidelines have been developed in this field by the institutions and the Ministry of Health of each country; these guidelines are seriously in need of integration and operation. In this study, we try to collect all the information and research done from the beginning of this pandemic in December 2019 - August 2022 concerning biosafety and protective measures, sample types, sampling methods, container, and storage solutions, sampling equipment, and sample storage and transportation for molecular testing of SARS-CoV-2.
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Affiliation(s)
- Mohammad Vasei
- Molecular Pathology and Cytogenetics Division, Pathology Department, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Cell-BasedTherapies Research Center, Digestive Disease Research Institute, Shari'ati Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Elham Jafari
- Molecular Pathology and Cytogenetics Division, Pathology Department, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Pathology and Stem Cells Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Vahid Falah Azad
- Molecular Pathology and Cytogenetics Division, Pathology Department, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Moeinadin Safavi
- Molecular Pathology and Cytogenetics Division, Pathology Department, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Sotoudeh
- Molecular Pathology and Cytogenetics Division, Pathology Department, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
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Dobrijević D, Vilotijević-Dautović G, Katanić J, Horvat M, Horvat Z, Pastor K. Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms. Viruses 2023; 15:1522. [PMID: 37515208 PMCID: PMC10383367 DOI: 10.3390/v15071522] [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: 06/14/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.
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Affiliation(s)
- Dejan Dobrijević
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Gordana Vilotijević-Dautović
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Jasmina Katanić
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Child and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Mirjana Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Zoltan Horvat
- Faculty of Civil Engineering Subotica, University of Novi Sad, 24000 Subotica, Serbia
| | - Kristian Pastor
- Faculty of Technology, University of Novi Sad, 21000 Novi Sad, Serbia
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Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics (Basel) 2023; 13:1749. [PMID: 37238232 PMCID: PMC10217633 DOI: 10.3390/diagnostics13101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Affiliation(s)
| | - Murat Koyuncu
- Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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Zhang F, Yang J, Wang Y, Cai M, Ouyang J, Li J. TT@MHA: A Machine Learning-based Webpage Tool for Discriminating Thalassemia Trait from Microcytic Hypochromic Anemia Patients. Clin Chim Acta 2023; 545:117368. [PMID: 37127232 DOI: 10.1016/j.cca.2023.117368] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/27/2023] [Accepted: 04/23/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND Iron deficiency anemia (IDA) and thalassemia trait (TT) are the most common causes of microcytic hypochromic anemia (MHA) and are endemic in lower resource settings and rural areas with poor medical infrastructure. Accurate discrimination between IDA and TT is an essential issue for MHA patients. Although various discriminant formulas have been reported, distinguishing between IDA and TT is still a challenging problem due to the diversity of anemic populations. METHODS We retrospectively collected laboratory data from 798 MHA patients. High proportions of α-TT (43.33%) and TT concomitant with IDA (TT&IDA) patients (14.04%) were found among TT patients. Five machine learning (ML) approaches, including Liner SVC (L-SVC), support vector machine learning (SVM), Extreme gradient boosting (XGB), Logistic Regression (LR), and Random Forest (RF), were applied to develop a discriminant model. Performance was assessed and compared with six existing discriminant formulas. RESULTS The RF model was chosen as the discriminant algorithm, namely TT@MHA. TT@MHA was tested in an interlaboratory cohort with a sensitivity, specificity, accuracy, and AUC of 91.91%, 91.00%, 91.53%, and 0.942, respectively. A webpage tool of TT@MHA (https://dxonline.deepwise.com/prediction/index.html?baseUrl=%2Fapi%2F&id=26408&topicName=undefined&from=share&platformType=wisdom) was developed to facilitate the healthcare providers in rural areas. CONCLUSION The ML-based TT@MHA algorithm, with high sensitivity and specificity, could help discriminate TT patients from MHA patients, especially in populations with high proportions of α-TT patients and TT&IDA patients. Moreover, a user-friendly webpage tool for TT@MHA could facilitate healthcare providers in rural areas where advanced technologies are not accessible.
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Affiliation(s)
- Fan Zhang
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Jing Yang
- Department of Medical Laboratory, Second Affiliated Hospital of Guangzhou Medical University, 250 Changgang Zhong Road, 510260, China
| | - Yang Wang
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Manyi Cai
- BGI Genomics Co.,Ltd, National Gene Bank of Guanyinshan Park, Jinsha Road, Dapeng Street, Dapeng New District, Shenzhen, Guangdong Province, 518120, China
| | - Juan Ouyang
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China.
| | - JunXun Li
- Department of Medical Laboratory, First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China.
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12
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Mohammedqasem R, Mohammedqasim H, Asad Ali Biabani S, Ata O, Alomary MN, Almehmadi M, Amer Alsairi A, Azam Ansari M. Multi-objective deep learning framework for COVID-19 dataset problems. JOURNAL OF KING SAUD UNIVERSITY. SCIENCE 2023; 35:102527. [PMID: 36590237 PMCID: PMC9795799 DOI: 10.1016/j.jksus.2022.102527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 12/08/2022] [Accepted: 12/23/2022] [Indexed: 05/28/2023]
Abstract
Background It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes. Methods This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN). Results The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%. Conclusions The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.
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Affiliation(s)
- Roa'a Mohammedqasem
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Hayder Mohammedqasim
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Sardar Asad Ali Biabani
- Science and Technology Unit, Umm Al- Qura University, Makkah, Saudi Arabia & Deanship of Scientific Research, Umm Al- Qura University, Makkah, Saudi Arabia
| | - Oguz Ata
- Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
| | - Mohammad N Alomary
- National Centre for Biotechnology, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Mazen Almehmadi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Ahad Amer Alsairi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Mohammad Azam Ansari
- Department of Epidemic Disease Research, Institute for Research & Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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13
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Gürsoy E, Kaya Y. An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works. MULTIMEDIA SYSTEMS 2023; 29:1603-1627. [PMID: 37261262 PMCID: PMC10039775 DOI: 10.1007/s00530-023-01083-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 03/20/2023] [Indexed: 06/02/2023]
Abstract
The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.
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Affiliation(s)
- Ercan Gürsoy
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
| | - Yasin Kaya
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Turkey
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14
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Deebak BD, Al-Turjman F. EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats. SENSORS (BASEL, SWITZERLAND) 2023; 23:2995. [PMID: 36991706 PMCID: PMC10051552 DOI: 10.3390/s23062995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/11/2022] [Accepted: 12/30/2022] [Indexed: 06/19/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient's body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (~98.3%) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease.
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Affiliation(s)
- B. D. Deebak
- Department of Computer Engineering, Gachon University, Gyeonggido, Seongnam 13120, Republic of Korea
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Deptartment, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
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15
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Behnoush AH, Khalaji A, Rezaee M, Momtahen S, Mansourian S, Bagheri J, Masoudkabir F, Hosseini K. Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery. Clin Cardiol 2023; 46:269-278. [PMID: 36588391 PMCID: PMC10018097 DOI: 10.1002/clc.23963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG. HYOTHESIS ML algorithms can significantly improve mortality prediction after CABG. METHODS Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models. RESULTS Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50-59 and 80-89 years), overweight, diabetic, and smoker subgroups of hypertensive patients. CONCLUSIONS All ML models had excellent performance in predicting 1-year mortality among CABG hypertension patients, while LR was the best regarding AUC. These models can help clinicians assess the risk of mortality in specific subgroups at higher risk (such as hypertensive ones).
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Affiliation(s)
- Amir Hossein Behnoush
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amirmohammad Khalaji
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Malihe Rezaee
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shahram Momtahen
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mansourian
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Jamshid Bagheri
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoudkabir
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Hosseini
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
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16
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Askari Nasab K, Mirzaei J, Zali A, Gholizadeh S, Akhlaghdoust M. Coronavirus diagnosis using cough sounds: Artificial intelligence approaches. Front Artif Intell 2023; 6:1100112. [PMID: 36872932 PMCID: PMC9975504 DOI: 10.3389/frai.2023.1100112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/24/2023] [Indexed: 02/17/2023] Open
Abstract
Introduction The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing. Method In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected" neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19. Result With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies. Conclusion These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.
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Affiliation(s)
- Kazem Askari Nasab
- Materials Science and Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Jamal Mirzaei
- Infectious Disease Research Center, Department of Infectious Diseases, Aja University of Medical Sciences, Tehran, Iran.,Infectious Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Zali
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Meisam Akhlaghdoust
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,USERN Office, Functional Neurosurgery Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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17
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Sharma A, Mishra PK. Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images. PATTERN RECOGNITION 2022; 131:108826. [PMID: 35698723 PMCID: PMC9170279 DOI: 10.1016/j.patcog.2022.108826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 04/24/2022] [Accepted: 06/02/2022] [Indexed: 05/17/2023]
Abstract
The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity.
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Affiliation(s)
- Ajay Sharma
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India
| | - Pramod Kumar Mishra
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India
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18
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Ayadi M, Ksibi A, Al-Rasheed A, Soufiene BO. COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images. Healthcare (Basel) 2022; 10:healthcare10102072. [PMID: 36292519 PMCID: PMC9601977 DOI: 10.3390/healthcare10102072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/10/2022] [Accepted: 10/16/2022] [Indexed: 12/21/2022] Open
Abstract
The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus. In the literature, various models based on machine learning (ML) and deep learning (DL) are introduced to detect many pneumonias using chest X-ray images. The cornerstone in this paper is the use of pretrained deep learning CNN architectures to construct an automated system for COVID-19 detection and diagnosis. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pre-trained CNN models, AlexNet and Xception. Hence, we propose COVID-AleXception: a neural network that is a concatenation of the AlexNet and Xception models for the overall improvement of the prediction capability of this pandemic. To evaluate the proposed model and build a dataset of large-scale X-ray images, there was a careful selection of multiple X-ray images from several sources. The COVID-AleXception model can achieve a classification accuracy of 98.68%, which shows the superiority of the proposed model over AlexNet and Xception that achieved a classification accuracy of 94.86% and 95.63%, respectively. The performance results of this proposed model demonstrate its pertinence to help radiologists diagnose COVID-19 more quickly.
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Affiliation(s)
- Manel Ayadi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Amel Ksibi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence:
| | - Amal Al-Rasheed
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ben Othman Soufiene
- Prince Laboratory Research, ISITcom (Institut Supérieur d’Informatique et des Techniques de Communication de Hammam Sousse), University of Sousse, Hammam Sousse 4023, Tunisia
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19
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Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters. INFORMATION 2022. [DOI: 10.3390/info13070330] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases.
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20
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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21
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Mohammedqasem R, Mohammedqasim H, Ata O. Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 100:107971. [PMID: 35399912 PMCID: PMC8985446 DOI: 10.1016/j.compeleceng.2022.107971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 05/03/2023]
Abstract
The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.
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Key Words
- ANN, Artificial Neural Network
- AUC, Area Under Curve
- CNN, Convolutional Neural Network
- COVID-19
- COVID-19, Coronavirus disease
- DL, Deep learning
- Imbalanced Dataset
- Internet of Things
- IoT, Internet of Things
- ML, Machine learning
- RFE, Recursive Feature Elimination
- RNN, Recurrent Neural Network
- Recursive feature elimination
- SMOTE, Synthetic Minority Oversampling Technique
- Synthetic minority oversampling technique
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Affiliation(s)
- Roa'a Mohammedqasem
- Department Of ECE, Institute Of Science, Altinbas University, Istanbul, Turkey
| | | | - Oguz Ata
- Department Of ECE, Institute Of Science, Altinbas University, Istanbul, Turkey
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