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Alathari MJA, Mashhadany YA, Bakar AAA, Mokhtar MHH, Bin Zan MSD, Arsad N. COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset. J Virol Methods 2024; 330:115011. [PMID: 39154936 DOI: 10.1016/j.jviromet.2024.115011] [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: 05/17/2024] [Revised: 07/14/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combines convolutional neural networks (CNN) with a bi-directional long short-term memory (Bi-LSTM) network, in conjunction with fiber optic data for SARS-CoV-2 Immunoglobulin G (IgG) antibodies. Our research introduces a comprehensive data preprocessing pipeline and evaluates the performance of four different deep learning (DL) algorithms: CNN, CNN-RNN, BiLSTM, and CNN-BiLSTM, in classifying samples as positive or negative for the COVID-19 virus. Among these, the CNN-BiLSTM classifier demonstrated superior performance on the training datasets, achieving an accuracy of 89 %, a recall of 88 %, a precision of 90 %, an F1-score of 89 %, a specificity of 90 %, a geometric mean (G-mean) of 89 %, and a receiver operating characteristic (ROC) of 96 %. In addition, the achieved classification results were compared with those reported in the literature. The findings indicate that the proposed model has promising potential for classifying COVID-19 and could serve as a valuable tool for healthcare professionals. The use of IgG antibodies to detect the virus enhances the specificity and accuracy of the diagnostic tool.
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Affiliation(s)
- Mohammed Jawad Ahmed Alathari
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Yousif Al Mashhadany
- Department of Electrical Engineering, College of Engineering, Anbar University, Anbar 00964, Iraq.
| | - Ahmad Ashrif A Bakar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Hadri Hafiz Mokhtar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Saiful Dzulkefly Bin Zan
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Norhana Arsad
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
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Lian J, Huang F, Huang X, Lau KYY, Ng KS, Chu CCF, Lam SC, Koohli-Moghadam M, Vardhanabhuti V. Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset. BMC Infect Dis 2024; 24:803. [PMID: 39123113 PMCID: PMC11313168 DOI: 10.1186/s12879-024-09699-x] [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: 04/10/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accurate prediction model difficult due to the imbalanced nature of the dataset. This study introduces an innovative application of graph convolutional networks (GCNs) to predict COVID-19 patient survival using a highly imbalanced dataset. Unlike traditional models, GCNs leverage structural relationships within the data, enhancing predictive accuracy and robustness. By integrating demographic and laboratory data into a GCN framework, our approach addresses class imbalance and demonstrates significant improvements in prediction accuracy. METHODS The cohort included all consecutive positive COVID-19 patients fulfilling study criteria admitted to 42 public hospitals in Hong Kong between January 23 and December 31, 2020 (n = 7,606). We proposed the population-based graph convolutional neural network (GCN) model which took blood test results, age and sex as inputs to predict the survival outcomes. Furthermore, we compared our proposed model to the Cox Proportional Hazard (CPH) model, conventional machine learning models, and oversampling machine learning models. Additionally, a subgroup analysis was performed on the test set in order to acquire a deeper understanding of the relationship between each patient node and its neighbours, revealing possible underlying causes of the inaccurate predictions. RESULTS The GCN model was the top-performing model, with an AUC of 0.944, considerably outperforming all other models (p < 0.05), including the oversampled CPH model (0.708), linear regression (0.877), Linear Discriminant Analysis (0.860), K-nearest neighbours (0.834), Gaussian predictor (0.745) and support vector machine (0.847). With Kaplan-Meier estimates, the GCN model demonstrated good discriminability between low- and high-risk individuals (p < 0.0001). Based on subanalysis using the weighted-in score, although the GCN model was able to discriminate well between different predicted groups, the separation was inadequate between false negative (FN) and true negative (TN) groups. CONCLUSION The GCN model considerably outperformed all other machine learning methods and baseline CPH models. Thus, when applied to this imbalanced COVID survival dataset, adopting a population graph representation may be an approach to achieving good prediction.
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Affiliation(s)
- Jie Lian
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Fan Huang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xinhai Huang
- Faculty of Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Kitty Yu-Yeung Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Kei Shing Ng
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Carlin Chun Fai Chu
- Department of Computing, The Hang Seng University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Simon Ching Lam
- School of Nursing, Tung Wah College, Ho Man Tin, Hong Kong SAR, China
| | - Mohamad Koohli-Moghadam
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
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Xing Z, Chen H, Alman AC. Discriminating insulin resistance in middle-aged nondiabetic women using machine learning approaches. AIMS Public Health 2024; 11:667-687. [PMID: 39027391 PMCID: PMC11252584 DOI: 10.3934/publichealth.2024034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 07/20/2024] Open
Abstract
Objective We employed machine learning algorithms to discriminate insulin resistance (IR) in middle-aged nondiabetic women. Methods The data was from the National Health and Nutrition Examination Survey (2007-2018). The study subjects were 2084 nondiabetic women aged 45-64. The analysis included 48 predictors. We randomly divided the data into training (n = 1667) and testing (n = 417) datasets. Four machine learning techniques were employed to discriminate IR: extreme gradient boosting (XGBoosting), random forest (RF), gradient boosting machine (GBM), and decision tree (DT). The area under the curve (AUC) of receiver operating characteristic (ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were compared as performance metrics to select the optimal technique. Results The XGBoosting algorithm achieved a relatively high AUC of 0.93 in the training dataset and 0.86 in the testing dataset to discriminate IR using 48 predictors and was followed by the RF, GBM, and DT models. After selecting the top five predictors to build models, the XGBoost algorithm with the AUC of 0.90 (training dataset) and 0.86 (testing dataset) remained the optimal prediction model. The SHapley Additive exPlanations (SHAP) values revealed the associations between the five predictors and IR, namely BMI (strongly positive impact on IR), fasting glucose (strongly positive), HDL-C (medium negative), triglycerides (medium positive), and glycohemoglobin (medium positive). The threshold values for identifying IR were 29 kg/m2, 100 mg/dL, 54.5 mg/dL, 89 mg/dL, and 5.6% for BMI, glucose, HDL-C, triglycerides, and glycohemoglobin, respectively. Conclusion The XGBoosting algorithm demonstrated superior performance metrics for discriminating IR in middle-aged nondiabetic women, with BMI, glucose, HDL-C, glycohemoglobin, and triglycerides as the top five predictors.
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Affiliation(s)
- Zailing Xing
- College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC 56, Tampa, FL 33612, USA
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Zoncapè M, Carlin M, Bicego M, Simonetti A, Ceruti V, Mantovani A, Inglese F, Zamboni G, Sartorio A, Minuz P, Romano S, Crisafulli E, Sacerdoti D, Fava C, Dalbeni A. Metabolic-associated fatty liver disease and liver fibrosis scores as COVID-19 outcome predictors: a machine-learning application. Intern Emerg Med 2023; 18:2063-2073. [PMID: 37268769 PMCID: PMC10238243 DOI: 10.1007/s11739-023-03316-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 05/15/2023] [Indexed: 06/04/2023]
Abstract
Patients with COVID-19 and metabolic-dysfunction associated fatty liver disease (MAFLD) appear to be at higher risk for severe manifestations, especially in the youngest decades. Our aim was to examine whether patients with MAFLD and/or with increased liver fibrosis scores (FIB-4) are at risk for severe COVID-19 illness, using a machine learning (ML) model. Six hundred and seventy two patients were enrolled for SARS-CoV-2 pneumonia between February 2020 and May 2021. Steatosis was detected by ultrasound or computed tomography (CT). ML model valuated the risks of both in-hospital death and prolonged hospitalizations (> 28 days), considering MAFLD, blood hepatic profile (HP), and FIB-4 score. 49.6% had MAFLD. The accuracy in predicting in-hospital death was 0.709 for the HP alone and 0.721 for HP + FIB-4; in the 55-75 age subgroup, 0.842/0.855; in the MAFLD subgroup, 0.739/ 0.772; in the MAFLD 55-75 years, 0.825/0.833. Similar results were obtained when considering the accuracy in predicting prolonged hospitalization. In our cohort of COVID-19 patients, the presence of a worse HP and a higher FIB-4 correlated with a higher risk of death and prolonged hospitalization, regardless of the presence of MAFLD. These findings could improve the clinical risk stratification of patients diagnosed with SARS-CoV-2 pneumonia.
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Affiliation(s)
- Mirko Zoncapè
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy.
- Liver Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy.
| | - Michele Carlin
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Manuele Bicego
- Department of Computer Science, University of Verona, Verona, Italy
| | - Andrea Simonetti
- Department of Computer Science, University of Verona, Verona, Italy
| | - Vittoria Ceruti
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Anna Mantovani
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
- Liver Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | | | - Giulia Zamboni
- Institute of Radiology, Department of Diagnostics and Public Health, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Andrea Sartorio
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Pietro Minuz
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Simone Romano
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Ernesto Crisafulli
- Division of Emergency Unit and Covid Unit, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy
| | - David Sacerdoti
- Liver Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Cristiano Fava
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Andrea Dalbeni
- Division of General Medicine C, Covid Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
- Liver Unit, Department of Medicine, University of Verona, Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
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Kidambi Raju S, Ramaswamy S, Eid MM, Gopalan S, Karim FK, Marappan R, Khafaga DS. Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection. Bioengineering (Basel) 2023; 10:880. [PMID: 37508907 PMCID: PMC10376564 DOI: 10.3390/bioengineering10070880] [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: 06/06/2023] [Revised: 07/01/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission's stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.
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Affiliation(s)
| | | | - Marwa M Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt
| | | | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Raja Marappan
- School of Computing, SASTRA Deemed University, Thanjavur 613401, India
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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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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Cabrera Alvargonzález J, Larrañaga Janeiro A, Pérez Castro S, Martínez Torres J, Martínez Lamas L, Daviña Nuñez C, Del Campo-Pérez V, Suarez Luque S, Regueiro García B, Porteiro Fresco J. Proof of concept of the potential of a machine learning algorithm to extract new information from conventional SARS-CoV-2 rRT-PCR results. Sci Rep 2023; 13:7786. [PMID: 37179356 PMCID: PMC10182547 DOI: 10.1038/s41598-023-34882-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/09/2023] [Indexed: 05/15/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges modern society has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Overall, this study suggests that there is valuable residual information in the rRT-PCR positive samples that can be used to identify patterns in the development of the SARS-CoV-2 pandemic. The successful application of supervised classification algorithms to detect these patterns demonstrates the potential of machine learning techniques to aid in understanding the spread of the virus and its variants.
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Affiliation(s)
- Jorge Cabrera Alvargonzález
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
- Universidade de Vigo, Vigo, Spain
| | | | - Sonia Pérez Castro
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
- Universidade de Vigo, Vigo, Spain
| | - Javier Martínez Torres
- Applied Mathematics I, Telecommunications Engineering School, Universidad de Vigo, 36310, Vigo, Spain
| | - Lucía Martínez Lamas
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
| | - Carlos Daviña Nuñez
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Víctor Del Campo-Pérez
- Department of Preventive Medicine and Public Health, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
| | - Silvia Suarez Luque
- Dirección Xeral de Saúde Pública, Consellería de Sanidade, Xunta de Galicia, Santiago de Compostela, A Coruña, Spain
| | - Benito Regueiro García
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
- Microbiology and Parasitology Department, Medicine and Odontology, Universidade de Santiago, Santiago de Compostela, Spain
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Zhang HP, Sun YL, Wang YF, Yazici D, Azkur D, Ogulur I, Azkur AK, Yang ZW, Chen XX, Zhang AZ, Hu JQ, Liu GH, Akdis M, Akdis CA, Gao YD. Recent developments in the immunopathology of COVID-19. Allergy 2023; 78:369-388. [PMID: 36420736 PMCID: PMC10108124 DOI: 10.1111/all.15593] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 11/01/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022]
Abstract
There has been an important change in the clinical characteristics and immune profile of Coronavirus disease 2019 (COVID-19) patients during the pandemic thanks to the extensive vaccination programs. Here, we highlight recent studies on COVID-19, from the clinical and immunological characteristics to the protective and risk factors for severity and mortality of COVID-19. The efficacy of the COVID-19 vaccines and potential allergic reactions after administration are also discussed. The occurrence of new variants of concerns such as Omicron BA.2, BA.4, and BA.5 and the global administration of COVID-19 vaccines have changed the clinical scenario of COVID-19. Multisystem inflammatory syndrome in children (MIS-C) may cause severe and heterogeneous disease but with a lower mortality rate. Perturbations in immunity of T cells, B cells, and mast cells, as well as autoantibodies and metabolic reprogramming may contribute to the long-term symptoms of COVID-19. There is conflicting evidence about whether atopic diseases, such as allergic asthma and rhinitis, are associated with a lower susceptibility and better outcomes of COVID-19. At the beginning of pandemic, the European Academy of Allergy and Clinical Immunology (EAACI) developed guidelines that provided timely information for the management of allergic diseases and preventive measures to reduce transmission in the allergic clinics. The global distribution of COVID-19 vaccines and emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with reduced pathogenic potential dramatically decreased the morbidity, severity, and mortality of COVID-19. Nevertheless, breakthrough infection remains a challenge for disease control. Hypersensitivity reactions (HSR) to COVID-19 vaccines are low compared to other vaccines, and these were addressed in EAACI statements that provided indications for the management of allergic reactions, including anaphylaxis to COVID-19 vaccines. We have gained a depth knowledge and experience in the over 2 years since the start of the pandemic, and yet a full eradication of SARS-CoV-2 is not on the horizon. Novel strategies are warranted to prevent severe disease in high-risk groups, the development of MIS-C and long COVID-19.
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Affiliation(s)
- Huan-Ping Zhang
- Department of Allergology, Shanxi Bethune Hospital, Shanxi Academy of Medical Science, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Yuan-Li Sun
- Department of Allergology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yan-Fen Wang
- Department of Pediatrics, Shanxi Bethune Hospital, Shanxi Academy of Medical Science, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Duygu Yazici
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Dilek Azkur
- Division of Pediatric Allergy and Immunology, Department of Pediatrics, Faculty of Medicine, University of Kirikkale, Kirikkale, Turkey
| | - Ismail Ogulur
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Ahmet Kursat Azkur
- Department of Virology, Faculty of Veterinary Medicine, University of Kirikkale, Kirikkale, Turkey
| | - Zhao-Wei Yang
- Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiao-Xue Chen
- Department of Allergology, Shanxi Bethune Hospital, Shanxi Academy of Medical Science, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Ai-Zhi Zhang
- Intensive Care Unit, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Jia-Qian Hu
- Department of Allergology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Guang-Hui Liu
- Department of Allergology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Mübeccel Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Cezmi A Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Ya-Dong Gao
- Department of Allergology, Zhongnan Hospital of Wuhan University, Wuhan, China
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Styrzynski F, Zhakparov D, Schmid M, Roqueiro D, Lukasik Z, Solek J, Nowicki J, Dobrogowski M, Makowska J, Sokolowska M, Baerenfaller K. Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study. Infect Dis Ther 2023; 12:111-129. [PMID: 36333475 PMCID: PMC9638383 DOI: 10.1007/s40121-022-00707-8] [Citation(s) in RCA: 3] [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: 07/25/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
INTRODUCTION In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. METHODS This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. RESULTS We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. CONCLUSIONS Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.
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Affiliation(s)
- Filip Styrzynski
- Department of Rheumatology with Subdepartment of Internal Medicine, Medical University of Lodz, 90-419, Lodz, Poland
| | - Damir Zhakparov
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265, Davos, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Schmid
- University of Applied Sciences of the Grisons, 7000, Chur, Switzerland
| | - Damian Roqueiro
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
| | - Zuzanna Lukasik
- Department of Rheumatology with Subdepartment of Internal Medicine, Medical University of Lodz, 90-419, Lodz, Poland
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265, Davos, Switzerland
| | - Julia Solek
- Department of Pathology, Chair of Oncology, Medical University of Lodz, 90-419, Lodz, Poland
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 90-419, Lodz, Poland
| | - Jakub Nowicki
- Department of Paediatrics, Newborn Pathology and Bone Metabolic Diseases, Medical University of Lodz, 90-419, Lodz, Poland
| | - Milosz Dobrogowski
- Maria Sklodowska-Curie Specialty Voivodeship Hospital, 95-100, Zgierz, Poland
| | - Joanna Makowska
- Department of Rheumatology with Subdepartment of Internal Medicine, Medical University of Lodz, 90-419, Lodz, Poland.
| | - Milena Sokolowska
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265, Davos, Switzerland.
- Christine Kühne - Center for Allergy Research and Education (CK-CARE), 7265, Davos, Switzerland.
| | - Katja Baerenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265, Davos, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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10
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Dritsas E, Trigka M. Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2. SENSORS (BASEL, SWITZERLAND) 2022; 23:s23010040. [PMID: 36616638 PMCID: PMC9824026 DOI: 10.3390/s23010040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 06/12/2023]
Abstract
The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%.
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11
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Kistenev YV, Vrazhnov DA, Shnaider EE, Zuhayri H. Predictive models for COVID-19 detection using routine blood tests and machine learning. Heliyon 2022; 8:e11185. [PMID: 36311357 PMCID: PMC9595489 DOI: 10.1016/j.heliyon.2022.e11185] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/25/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022] Open
Abstract
The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.
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Affiliation(s)
- Yury V. Kistenev
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin Av., 634050 Tomsk, Russia
| | - Denis A. Vrazhnov
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin Av., 634050 Tomsk, Russia
| | - Ekaterina E. Shnaider
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin Av., 634050 Tomsk, Russia
| | - Hala Zuhayri
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin Av., 634050 Tomsk, Russia
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12
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Alsaaidah B, Al-Hadidi MR, Al-Nsour H, Masadeh R, AlZubi N. Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022; 8:267. [PMID: 36286361 PMCID: PMC9604704 DOI: 10.3390/jimaging8100267] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 01/14/2023] Open
Abstract
The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
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Affiliation(s)
- Bayan Alsaaidah
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Moh’d Rasoul Al-Hadidi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
| | - Heba Al-Nsour
- Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information Technology and Communications, Al-Balqa Applied University, Salt 19117, Jordan
| | - Raja Masadeh
- Computer Science Department, The World Islamic Sciences and Education University, Amman 11947, Jordan
| | - Nael AlZubi
- Department of Electrical Engineering, Electrical Power Engineering and Computer Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt 19117, Jordan
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13
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Cremades-Martínez P, Parker LA, Chilet-Rosell E, Lumbreras B. Evaluation of Diagnostic Strategies for Identifying SARS-CoV-2 Infection in Clinical Practice: a Systematic Review and Compliance with the Standards for Reporting Diagnostic Accuracy Studies Guideline (STARD). Microbiol Spectr 2022; 10:e0030022. [PMID: 35699441 PMCID: PMC9430610 DOI: 10.1128/spectrum.00300-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/12/2022] [Indexed: 11/21/2022] Open
Abstract
We aimed to review strategies for identifying SARS-CoV-2 infection before the availability of molecular test results, and to assess the reporting quality of the studies identified through the application of the STARD guideline. We screened 3,821 articles published until 30 April 2021, of which 23 met the inclusion criteria: including at least two diagnostic variables, being designed for use in clinical practice or in a public health context and providing diagnostic accuracy rates. Data extraction and application of STARD criteria were performed independently by two researchers and discrepancies were discussed with a third author. Most of the studies (16, 69.6%) included symptomatic patients with suspected infection, six studies (26.1%) included patients already diagnosed and one study (4.3%) included individuals with close contact to a COVID-positive patient. The main variables considered in the studies, which included symptomatic patients, were imaging and demographic characteristics, symptoms, and lymphocyte count. The values for area under the receiver operating characteristic curve (AUC)ranged from 53-97.4. Seven studies (30.4%) validated the diagnostic model in an independent sample. The average number of STARD criteria fulfilled was 17.6 (maximum, 27 and minimum, 5). High diagnostic accuracy values are shown when more than one diagnostic variable is considered, mainly imaging and demographic characteristics, symptoms, and lymphocyte count. This could offer the potential to identify individuals with SARS-CoV-2 infection with high accuracy when molecular testing is not available. However, external validation for developed models and evaluations in populations as similar as possible to those in which they will be applied is urgently needed. IMPORTANCE According to this review, the inclusion of more than one diagnostic test in the diagnostic process for COVID-19 infection shows high diagnostic accuracy values. Imaging characteristics, patients' symptoms, demographic characteristics, and lymphocyte count were the variables most frequently included in the diagnostic models. However, developed models should be externally validated before reaching conclusions on their utility in practice. In addition, it is important to bear in mind that the test should be evaluated in populations as similar as possible to those in which it will be applied in practice.
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Affiliation(s)
| | - Lucy A. Parker
- Public Health, History of Medicine and Gynecology Department, Miguel Hernandez University, Alicante, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Elisa Chilet-Rosell
- Public Health, History of Medicine and Gynecology Department, Miguel Hernandez University, Alicante, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Blanca Lumbreras
- Public Health, History of Medicine and Gynecology Department, Miguel Hernandez University, Alicante, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
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14
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Chamberlin JH, Aquino G, Nance S, Wortham A, Leaphart N, Paladugu N, Brady S, Baird H, Fiegel M, Fitzpatrick L, Kocher M, Ghesu F, Mansoor A, Hoelzer P, Zimmermann M, James WE, Dennis DJ, Houston BA, Kabakus IM, Baruah D, Schoepf UJ, Burt JR. Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning. BMC Infect Dis 2022; 22:637. [PMID: 35864468 PMCID: PMC9301895 DOI: 10.1186/s12879-022-07617-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 07/14/2022] [Indexed: 11/10/2022] Open
Abstract
Background Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. Methods This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. Results Overall ICC was 0.820 (95% CI 0.790–0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861–0.920) for the neural network and 0.936 (95% CI 0.918–0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). Conclusion The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.
Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07617-7.
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Affiliation(s)
- Jordan H Chamberlin
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Gilberto Aquino
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Sophia Nance
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew Wortham
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Nathan Leaphart
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Namrata Paladugu
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Sean Brady
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Henry Baird
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Matthew Fiegel
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Logan Fitzpatrick
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Madison Kocher
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | | | | | | | | | - W Ennis James
- Department of Internal Medicine, Division of Pulmonary, Critical Care, Allergy & Sleep Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - D Jameson Dennis
- Department of Internal Medicine, Division of Pulmonary, Critical Care, Allergy & Sleep Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Brian A Houston
- Department of Internal Medicine, Division of Cardiology, Medical University of South Carolina, Charleston, SC, USA
| | - Ismail M Kabakus
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Dhiraj Baruah
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - U Joseph Schoepf
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Jeremy R Burt
- Department of Radiology and Radiologic Sciences, Division of Cardiothoracic Radiology, Medical University of South Carolina, Charleston, SC, USA. .,MUSC-ART, Cardiothoracic Imaging, 25 Courtenay Drive, MSC 226, 2nd Floor, Rm 2256, Charleston, SC, 29425, USA.
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15
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Abayomi-Alli OO, Damaševičius R, Maskeliūnas R, Misra S. An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples. SENSORS 2022; 22:s22062224. [PMID: 35336395 PMCID: PMC8955536 DOI: 10.3390/s22062224] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 02/04/2023]
Abstract
Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.
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Affiliation(s)
- Olusola O. Abayomi-Alli
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania;
- Correspondence:
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Sanjay Misra
- Department of Computer Science and Communication, Ostfold University College, 3001 Halden, Norway;
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16
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Lau KYY, Ng KS, Kwok KW, Tsia KKM, Sin CF, Lam CW, Vardhanabhuti V. An Unsupervised Machine Learning Clustering and Prediction of Differential Clinical Phenotypes of COVID-19 Patients Based on Blood Tests—A Hong Kong Population Study. Front Med (Lausanne) 2022; 8:764934. [PMID: 35284429 PMCID: PMC8907521 DOI: 10.3389/fmed.2021.764934] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/27/2021] [Indexed: 01/08/2023] Open
Abstract
Background To better understand the different clinical phenotypes across the disease spectrum in patients with COVID-19 using an unsupervised machine learning clustering approach. Materials and Methods A population-based retrospective study was conducted utilizing demographics, clinical characteristics, comorbidities, and clinical outcomes of 7,606 COVID-19–positive patients on admission to public hospitals in Hong Kong in the year 2020. An unsupervised machine learning clustering was used to explore this large cohort. Results Four clusters of differing clinical phenotypes based on data at initial admission was derived in which 86.6% of the deceased cases were aggregated in one of the clusters without prior knowledge of their clinical outcomes. Other distinctive clinical characteristics of this cluster were old age and high concurrent comorbidities as well as laboratory characteristics of lower hemoglobin/hematocrit levels, higher neutrophil, C-reactive protein, lactate dehydrogenase, and creatinine levels. The clinical patterns captured by the cluster analysis was validated on other temporally distinct cohorts in 2021. The phenotypes aligned with existing literature. Conclusion The study demonstrated the usefulness of unsupervised machine learning techniques with the potential to uncover latent clinical phenotypes. It could serve as a more robust classification for patient triaging and patient-tailored treatment strategies.
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Affiliation(s)
- Kitty Yu-Yeung Lau
- Biomedical Engineering Programme, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Kei-Shing Ng
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ka-Wai Kwok
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Kevin Kin-Man Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Chun-Fung Sin
- Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ching-Wan Lam
- Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- *Correspondence: Varut Vardhanabhuti
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17
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Sahebkar A, Abbasifard M, Chaibakhsh S, Guest PC, Pourhoseingholi MA, Vahedian-Azimi A, Kesharwani P, Jamialahmadi T. A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes. Methods Mol Biol 2022; 2511:395-404. [PMID: 35838977 DOI: 10.1007/978-1-0716-2395-4_30] [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] [Indexed: 06/15/2023]
Abstract
There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.
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Affiliation(s)
- Amirhossein Sahebkar
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- School of Medicine, The University of Western Australia, Perth, Australia.
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Mitra Abbasifard
- Immunology of Infectious Diseases Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
- Department of Internal Medicine, Ali-Ibn Abi-Talib Hospital, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
| | - Samira Chaibakhsh
- Eye Research Center, The five Senses Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Paul C Guest
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Mohamad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Prashant Kesharwani
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Tannaz Jamialahmadi
- Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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18
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Guest PC, Popovic D, Steiner J. Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective. Methods Mol Biol 2022; 2511:37-50. [PMID: 35838950 DOI: 10.1007/978-1-0716-2395-4_3] [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] [Indexed: 06/15/2023]
Abstract
Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.
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Affiliation(s)
- Paul C Guest
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil.
| | - David Popovic
- Section of Forensic Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Johann Steiner
- Laboratory of Translational Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
- Department of Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Mental Health (DZP), Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Site Jena-Magdeburg-Halle, Magdeburg, Germany
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Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer. J Clin Med 2021; 11:jcm11010219. [PMID: 35011959 PMCID: PMC8746167 DOI: 10.3390/jcm11010219] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 12/22/2021] [Accepted: 12/24/2021] [Indexed: 12/21/2022] Open
Abstract
To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.
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Fusco R, Grassi R, Granata V, Setola SV, Grassi F, Cozzi D, Pecori B, Izzo F, Petrillo A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J Pers Med 2021; 11:993. [PMID: 34683133 PMCID: PMC8540782 DOI: 10.3390/jpm11100993] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. METHODS Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). RESULTS Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). CONCLUSIONS Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.
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Affiliation(s)
- Roberta Fusco
- IGEA SpA Medical Division—Oncology, Via Casarea 65, Casalnuovo di Napoli, 80013 Naples, Italy;
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
| | - Diletta Cozzi
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
| | - Biagio Pecori
- Division of Radiotherapy and Innovative Technologies, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
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