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Diel R, Nienhaus A. Cost-Benefit of Real-Time Multiplex PCR Testing of SARS-CoV-2 in German Hospitals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3447. [PMID: 36834141 PMCID: PMC9960777 DOI: 10.3390/ijerph20043447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/07/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The current Omicron COVID-19 pandemic has significant morbidity worldwide. OBJECTIVE Assess the cost-benefit relation of implementing PCR point-of-care (POCT) COVID-19 testing in the emergency rooms (ERs) of German hospitals and in the case of inpatient admission due to other acute illnesses. METHODS A deterministic decision-analytic model simulated the incremental costs of using the Savanna® Multiplex RT-PCR test compared to using clinical judgement alone to confirm or exclude COVID-19 in adult patients in German ERs prior to hospitalization or just prior to discharge. Direct and indirect costs were evaluated from the hospital perspective. Nasal or nasopharyngeal swabs of patients suspected to have COVID-19 by clinical judgement, but without POCT, were sent to external labs for RT-PCR testing. RESULTS In probabilistic sensitivity analysis, assuming a COVID-19 prevalence ranging between 15.6-41.2% and a hospitalization rate between 4.3-64.3%, implementing the Savanna® test saved, on average, €107 as compared to applying the clinical-judgement-only strategy. A revenue loss of €735 can be avoided when SARS-CoV-2 infection in patients coming unplanned to the hospital due to other acute illnesses are excluded immediately by POCT. CONCLUSIONS Using highly sensitive and specific PCR-POCT in patients suspected of COVID-19 infection at German ERs may significantly reduce hospital expenditures.
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Affiliation(s)
- Roland Diel
- Institute for Epidemiology, University Medical Hospital Schleswig-Holstein, Kiel, Airway Research Center North (ARCN), 24015 Kiel, Germany
- Lung Clinic Grosshansdorf, Germany, Airway Disease Center North (ARCN), German Center for Lung Research (DZL), 22949 Großhansdorf, Germany
- Institution for Statutory Accident Insurance and Prevention in the Health and Welfare Services (BGW), 22089 Hamburg, Germany
| | - Albert Nienhaus
- Institution for Statutory Accident Insurance and Prevention in the Health and Welfare Services (BGW), 22089 Hamburg, Germany
- Institute for Health Service Research in Dermatology and Nursing, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
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Chavda VP, Valu DD, Parikh PK, Tiwari N, Chhipa AS, Shukla S, Patel SS, Balar PC, Paiva-Santos AC, Patravale V. Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants. Vaccines (Basel) 2023; 11:374. [PMID: 36851252 PMCID: PMC9960989 DOI: 10.3390/vaccines11020374] [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: 12/15/2022] [Revised: 01/25/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
Accurate identification at an early stage of infection is critical for effective care of any infectious disease. The "coronavirus disease 2019 (COVID-19)" outbreak, caused by the virus "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", corresponds to the current and global pandemic, characterized by several developing variants, many of which are classified as variants of concern (VOCs) by the "World Health Organization (WHO, Geneva, Switzerland)". The primary diagnosis of infection is made using either the molecular technique of RT-PCR, which detects parts of the viral genome's RNA, or immunodiagnostic procedures, which identify viral proteins or antibodies generated by the host. As the demand for the RT-PCR test grew fast, several inexperienced producers joined the market with innovative kits, and an increasing number of laboratories joined the diagnostic field, rendering the test results increasingly prone to mistakes. It is difficult to determine how the outcomes of one unnoticed result could influence decisions about patient quarantine and social isolation, particularly when the patients themselves are health care providers. The development of point-of-care testing helps in the rapid in-field diagnosis of the disease, and such testing can also be used as a bedside monitor for mapping the progression of the disease in critical patients. In this review, we have provided the readers with available molecular diagnostic techniques and their pitfalls in detecting emerging VOCs of SARS-CoV-2, and lastly, we have discussed AI-ML- and nanotechnology-based smart diagnostic techniques for SARS-CoV-2 detection.
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Affiliation(s)
- Vivek P. Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Disha D. Valu
- Formulation and Drug Product Development, Biopharma Division, Intas Pharmaceutical Ltd., 3000-548 Moraiya, Ahmedabad 380054, Gujarat, India
| | - Palak K. Parikh
- Department of Pharmaceutical Chemistry and Quality Assurance, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Nikita Tiwari
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Mumbai 400019, Maharashtra, India
| | - Abu Sufiyan Chhipa
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad 382481, Gujarat, India
| | - Somanshi Shukla
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Mumbai 400019, Maharashtra, India
| | - Snehal S. Patel
- Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad 382481, Gujarat, India
| | - Pankti C. Balar
- Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Ana Cláudia Paiva-Santos
- Department of Pharmaceutical Technology, Faculty of Pharmacy of the University of Coimbra, University of Coimbra, 3000-548 Coimbra, Portugal
- REQUIMTE/LAQV, Group of Pharmaceutical Technology, Faculty of Pharmacy of the University of Coimbra, University of Coimbra, 3000-548 Coimbra, Portugal
| | - Vandana Patravale
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Mumbai 400019, Maharashtra, India
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103
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Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:diagnostics13040584. [PMID: 36832072 PMCID: PMC9955250 DOI: 10.3390/diagnostics13040584] [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: 12/20/2022] [Revised: 01/25/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
Because it is an accessible and routine image test, medical personnel commonly use a chest X-ray for COVID-19 infections. Artificial intelligence (AI) is now widely applied to improve the precision of routine image tests. Hence, we investigated the clinical merit of the chest X-ray to detect COVID-19 when assisted by AI. We used PubMed, Cochrane Library, MedRxiv, ArXiv, and Embase to search for relevant research published between 1 January 2020 and 30 May 2022. We collected essays that dissected AI-based measures used for patients diagnosed with COVID-19 and excluded research lacking measurements using relevant parameters (i.e., sensitivity, specificity, and area under curve). Two independent researchers summarized the information, and discords were eliminated by consensus. A random effects model was used to calculate the pooled sensitivities and specificities. The sensitivity of the included research studies was enhanced by eliminating research with possible heterogeneity. A summary receiver operating characteristic curve (SROC) was generated to investigate the diagnostic value for detecting COVID-19 patients. Nine studies were recruited in this analysis, including 39,603 subjects. The pooled sensitivity and specificity were estimated as 0.9472 (p = 0.0338, 95% CI 0.9009-0.9959) and 0.9610 (p < 0.0001, 95% CI 0.9428-0.9795), respectively. The area under the SROC was 0.98 (95% CI 0.94-1.00). The heterogeneity of diagnostic odds ratio was presented in the recruited studies (I2 = 36.212, p = 0.129). The AI-assisted chest X-ray scan for COVID-19 detection offered excellent diagnostic potential and broader application.
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104
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Fontes C, Corrigan C, Lütge C. Governing AI during a pandemic crisis: Initiatives at the EU level. TECHNOLOGY IN SOCIETY 2023; 72:102204. [PMID: 36777094 PMCID: PMC9894826 DOI: 10.1016/j.techsoc.2023.102204] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
After the outbreak of Covid-19, the European Commission (EC) promptly took the initiative to lead and coordinate a common European response. The actions unfolded in several directions, paving the way to the uptake of AI-related solutions and placing hope in these tools to face crises, namely of a public health and global nature. In this article, we focus on initiatives for the uptake of AI-related solutions from the experimental level towards implementation. The Repository of AI and Robotics solutions, launched in 2020, is an example of an initiative put forth to leverage and disseminate knowledge on AI, expanding the fields of application and fostering the development and adaptation of cutting-edge technologies to explore how they can assist in tackling specific tasks during a public health crisis. Using this database, the article outlines the promise of AI as a hope for handling specific needs and tasks and how the uptake of such technologies was accelerated during the Covid-19 pandemic. In extension, we frame initiatives for the uptake of AI-enabled solutions from a governance perspective, focusing on the establishment of frameworks for ethical and trustworthy AI by defining principles and standards that aim to protect the underlying values deemed fundamental.
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Affiliation(s)
- Catarina Fontes
- Technical University of Munich, School of Social Sciences and Technology, Institute for Ethics in Artificial Intelligence, München, 80333, Germany
| | - Caitlin Corrigan
- Technical University of Munich, School of Social Sciences and Technology, Institute for Ethics in Artificial Intelligence, München, 80333, Germany
| | - Christoph Lütge
- Technical University of Munich, School of Social Sciences and Technology, Institute for Ethics in Artificial Intelligence, München, 80333, Germany
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105
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Affiliation(s)
- Theresa C McLoud
- From the Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, MZ-FND 216, Boston, MA 02114-2696 (T.C.M.); and Department of Radiology, Mayo Clinic College of Medicine and Science, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
| | - Brent P Little
- From the Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit St, MZ-FND 216, Boston, MA 02114-2696 (T.C.M.); and Department of Radiology, Mayo Clinic College of Medicine and Science, Mayo Clinic Florida, Jacksonville, Fla (B.P.L.)
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106
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Aslani S, Jacob J. Utilisation of deep learning for COVID-19 diagnosis. Clin Radiol 2023; 78:150-157. [PMID: 36639173 PMCID: PMC9831845 DOI: 10.1016/j.crad.2022.11.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 01/12/2023]
Abstract
The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.
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Affiliation(s)
- S Aslani
- Centre for Medical Image Computing and Department of Respiratory Medicine, University College London, London, UK.
| | - J Jacob
- Centre for Medical Image Computing and Department of Respiratory Medicine, University College London, London, UK
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107
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Amini N, Mahdavi M, Choubdar H, Abedini A, Shalbaf A, Lashgari R. Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier. Comput Methods Biomech Biomed Engin 2023; 26:160-173. [PMID: 35297747 DOI: 10.1080/10255842.2022.2050906] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Early prediction of COVID-19 mortality outcome can decrease expiration risk by alerting healthcare personnel to assure efficient resource allocation and treatment planning. This study introduces a machine learning framework for the prediction of COVID-19 mortality using demographics, vital signs, and laboratory blood tests (complete blood count (CBC), coagulation, kidney, liver, blood gas, and general). 41 features from 244 COVID-19 patients were recorded on the first day of admission. In this study, first, the features in each of the eight categories were investigated. Afterward, features that have an area under the receiver operating characteristic curve (AUC) above 0.6 and the p-value criterion from the Wilcoxon rank-sum test below 0.005 were used as selected features for further analysis. Then five feature reduction methods, Forward Feature selection, minimum Redundancy Maximum Relevance, Relieff, Linear Discriminant Analysis, and Neighborhood Component Analysis were utilized to select the best combination of features. Finally, seven classifiers frameworks, random forest (RF), support vector machine, logistic regression (LR), K nearest neighbors, Artifical neural network, bagging, and boosting were used to predict the mortality outcome of COVID-19 patients. The results revealed that the combination of features in CBC and then vital signs had the highest mortality classification parameters, respectively. Furthermore, the RF classifier with hierarchical feature selection algorithms via Forward Feature selection had the highest classification power with an accuracy of 92.08 ± 2.56. Therefore, our proposed method can be confidently used as a valuable assistant prognostic tool to sieve patients with high mortality risks.
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Affiliation(s)
- Nasrin Amini
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahdi Mahdavi
- Institute of Medical Science and Technology (IMSAT), Shahid Beheshti University, Tehran, Iran.,School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hadi Choubdar
- Institute of Medical Science and Technology (IMSAT), Shahid Beheshti University, Tehran, Iran.,School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Atefeh Abedini
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Lashgari
- Institute of Medical Science and Technology (IMSAT), Shahid Beheshti University, Tehran, Iran
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108
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Zhao B, Zhai H, Shao H, Bi K, Zhu L. Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107295. [PMID: 36706562 PMCID: PMC9711896 DOI: 10.1016/j.cmpb.2022.107295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/10/2022] [Accepted: 11/29/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.
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Affiliation(s)
- Bingqiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China.
| | - Haiping Shao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Kexin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Ling Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
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109
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Millet K, Buehler F, Du G, Kokkoris M. Defending humankind: Anthropocentric bias in the appreciation of AI art. COMPUTERS IN HUMAN BEHAVIOR 2023. [DOI: 10.1016/j.chb.2023.107707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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110
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Huyut MT. Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models. Ing Rech Biomed 2023; 44:100725. [PMID: 35673548 PMCID: PMC9158375 DOI: 10.1016/j.irbm.2022.05.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 04/24/2022] [Accepted: 05/29/2022] [Indexed: 02/07/2023]
Abstract
Objectives When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission. Material and methods The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set. Results In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%. Conclusion The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.
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Affiliation(s)
- M T Huyut
- Department of Biostatistics and Medical Informatics, Medical Faculty, Erzincan Binali Yıldırım University, 24100, Erzincan, Turkey
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111
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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. J Cardiovasc Dev Dis 2023; 10:jcdd10020039. [PMID: 36826535 PMCID: PMC9967447 DOI: 10.3390/jcdd10020039] [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: 11/28/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.
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112
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Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing. Viruses 2023; 15:v15020304. [PMID: 36851519 PMCID: PMC9966023 DOI: 10.3390/v15020304] [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: 10/21/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT-PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT-PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT-PCR are used for diagnosis shows the possibility of nearly halving the time required for RT-PCR diagnosis.
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113
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Towards precision medicine: Omics approach for COVID-19. BIOSAFETY AND HEALTH 2023; 5:78-88. [PMID: 36687209 PMCID: PMC9846903 DOI: 10.1016/j.bsheal.2023.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/19/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.
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114
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Vinod DN, Prabaharan SRS. COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2667-2682. [PMID: 36685135 PMCID: PMC9843670 DOI: 10.1007/s11831-023-09882-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/05/2023] [Indexed: 05/29/2023]
Abstract
The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.
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Affiliation(s)
- Dasari Naga Vinod
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062 India
| | - S. R. S. Prabaharan
- Sathyabama Centre for Advanced Studies, Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, Tamil Nadu 600119 India
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115
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Afrash MR, Shafiee M, Kazemi-Arpanahi H. Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors. BMC Gastroenterol 2023; 23:6. [PMID: 36627564 PMCID: PMC9832798 DOI: 10.1186/s12876-022-02626-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors. METHODS This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models' performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation. RESULTS This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer. CONCLUSIONS The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.
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Affiliation(s)
- Mohammad Reza Afrash
- grid.411705.60000 0001 0166 0922Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Mohsen Shafiee
- Department of Nursing, Abadan University of Medical Sciences, Abadan, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
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Ullah N, Khan JA, El-Sappagh S, El-Rashidy N, Khan MS. A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network. Diagnostics (Basel) 2023; 13:162. [PMID: 36611454 PMCID: PMC9818310 DOI: 10.3390/diagnostics13010162] [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: 11/29/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.
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Affiliation(s)
- Naeem Ullah
- Department of Software Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan
| | - Javed Ali Khan
- Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Nora El-Rashidy
- Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafr Elsheikh 33516, Egypt
| | - Mohammad Sohail Khan
- Department of Computer Software Engineering, University of Engineering and Technology Mardan, Mardan 23200, Pakistan
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Chen Z, Liang Q, Wei Z, Chen X, Shi Q, Yu Z, Sun T. An Overview of In Vitro Biological Neural Networks for Robot Intelligence. CYBORG AND BIONIC SYSTEMS 2023; 4:0001. [PMID: 37040493 PMCID: PMC10076061 DOI: 10.34133/cbsystems.0001] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/17/2022] [Indexed: 01/12/2023] Open
Abstract
In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc. This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence. In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots. Next, we separate the intelligent behaviors into 2 parts according to whether they rely solely on the computing capacity (computing capacity-dependent) or depend also on the network plasticity (network plasticity-dependent), which are then expounded respectively, with a focus on those related to the realization of robot intelligence. Finally, the development trends and challenges of the BNN-based neurorobotic systems are discussed.
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Affiliation(s)
- Zhe Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
| | - Qian Liang
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zihou Wei
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Xie Chen
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Qing Shi
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zhiqiang Yu
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Tao Sun
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
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Zakariaee SS, Abdi AI, Naderi N, Babashahi M. Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023; 54:73. [PMCID: PMC10116092 DOI: 10.1186/s43055-023-01022-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 04/13/2023] [Indexed: 04/05/2024] Open
Abstract
Background The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet. Methods This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models. Results After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (± SD) age of participants was 57.22(± 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques. Conclusions The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.
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Affiliation(s)
- Seyed Salman Zakariaee
- Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
| | - Aza Ismail Abdi
- Department of Radiology, Erbil Medical Technical Institute, Erbil Polytechnic University, Erbil, Iraq
| | - Negar Naderi
- Department of Midwifery, Faculty of Nursing and Midwifery, Ilam University of Medical Sciences, Ilam, Iran
| | - Mashallah Babashahi
- Department of Pathology, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
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Aminu M, Yadav D, Hong L, Young E, Edelkamp P, Saad M, Salehjahromi M, Chen P, Sujit SJ, Chen MM, Sabloff B, Gladish G, de Groot PM, Godoy MCB, Cascone T, Vokes NI, Zhang J, Brock KK, Daver N, Woodman SE, Tawbi HA, Sheshadri A, Lee JJ, Jaffray D, Wu CC, Chung C, Wu J. Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19. Cancers (Basel) 2022; 15:275. [PMID: 36612278 PMCID: PMC9818576 DOI: 10.3390/cancers15010275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVES Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. METHODS We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. RESULTS These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. CONCLUSIONS Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.
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Affiliation(s)
- Muhammad Aminu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Divya Yadav
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Lingzhi Hong
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Elliana Young
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Paul Edelkamp
- Department of Enterprise Data Engineering & Analytics, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Maliazurina Saad
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Pingjun Chen
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Sheeba J. Sujit
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Melissa M. Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Bradley Sabloff
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Gregory Gladish
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Patricia M. de Groot
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Myrna C. B. Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Tina Cascone
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Kristy K. Brock
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
| | - Naval Daver
- Department of Leukemia, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Scott E. Woodman
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Hussein A. Tawbi
- Department of Melanoma Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | | | - Carol C. Wu
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Caroline Chung
- Office of the Chief Data Officer, MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX 77054, USA
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Phung KA, Nguyen TT, Wangad N, Baraheem S, Vo ND, Nguyen K. Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model. J Imaging 2022; 8:jimaging8120323. [PMID: 36547488 PMCID: PMC9786084 DOI: 10.3390/jimaging8120323] [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: 08/25/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively.
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Affiliation(s)
- Kim Anh Phung
- Department of Computer Science, University of Dayton, Dayton, OH 45469, USA
| | - Thuan Trong Nguyen
- Faculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam
| | - Nileshkumar Wangad
- Department of Computer Science, University of Dayton, Dayton, OH 45469, USA
| | - Samah Baraheem
- Department of Computer Science, University of Dayton, Dayton, OH 45469, USA
| | - Nguyen D. Vo
- Faculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam
| | - Khang Nguyen
- Faculty of Software Engineering, University of Information Technology, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 70000, Vietnam
- Correspondence:
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Afrash MR, Shanbehzadeh M, Kazemi-Arpanahi H. Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms. J Biomed Phys Eng 2022; 12:611-626. [PMID: 36569564 PMCID: PMC9759642 DOI: 10.31661/jbpe.v0i0.2105-1334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 01/20/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA). OBJECTIVE This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. MATERIAL AND METHODS In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models. RESULTS A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. CONCLUSION The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.
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Affiliation(s)
- Mohammad Reza Afrash
- PhD, Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- PhD, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- PhD, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- PhD, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Elkin ME, Zhu X. A machine learning study of COVID-19 serology and molecular tests and predictions. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2022; 26:100331. [PMID: 36281350 PMCID: PMC9583626 DOI: 10.1016/j.smhl.2022.100331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022]
Abstract
Serology and molecular tests are the two most commonly used methods for rapid COVID-19 infection testing. The two types of tests have different mechanisms to detect infection, by measuring the presence of viral SARS-CoV-2 RNA (molecular test) or detecting the presence of antibodies triggered by the SARS-CoV-2 virus (serology test). A handful of studies have shown that symptoms, combined with demographic and/or diagnosis features, can be helpful for the prediction of COVID-19 test outcomes. However, due to nature of the test, serology and molecular tests vary significantly. There is no existing study on the correlation between serology and molecular tests, and what type of symptoms are the key factors indicating the COVID-19 positive tests. In this study, we propose a machine learning based approach to study serology and molecular tests, and use features to predict test outcomes. A total of 2,467 donors, each tested using one or multiple types of COVID-19 tests, are collected as our testbed. By cross checking test types and results, we study correlation between serology and molecular tests. For test outcome prediction, we label 2,467 donors as positive or negative, by using their serology or molecular test results, and create symptom features to represent each donor for learning. Because COVID-19 produces a wide range of symptoms and the data collection process is essentially error prone, we group similar symptoms into bins. This decreases the feature space and sparsity. Using binned symptoms, combined with demographic features, we train five classification algorithms to predict COVID-19 test results. Experiments show that XGBoost achieves the best performance with 76.85% accuracy and 81.4% AUC scores, demonstrating that symptoms are indeed helpful for predicting COVID-19 test outcomes. Our study investigates the relationship between serology and molecular tests, identifies meaningful symptom features associated with COVID-19 infection, and also provides a way for rapid screening and cost effective detection of COVID-19 infection.
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Affiliation(s)
- Magdalyn E Elkin
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Xingquan Zhu
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
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Ozsahin DU, Isa NA, Uzun B. The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis. Diagnostics (Basel) 2022; 12:2943. [PMID: 36552949 PMCID: PMC9777320 DOI: 10.3390/diagnostics12122943] [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: 10/15/2022] [Revised: 11/12/2022] [Accepted: 11/18/2022] [Indexed: 11/26/2022] Open
Abstract
Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60-70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Sciences, Sharjah University, Sharjah P.O. Box 27272, United Arab Emirates
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Nuhu Abdulhaqq Isa
- Department of Biomedical Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Biomedical Engineering, College of Health Science and Technology, Keffi 961101, Keffi Nasarawa State, Nigeria
| | - Berna Uzun
- Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Statistics, Carlos III Madrid University, 28903 Getafe, Madrid, Spain
- Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
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Duong LT, Nguyen PT, Iovino L, Flammini M. Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning. Appl Soft Comput 2022; 132:109851. [PMCID: PMC9686054 DOI: 10.1016/j.asoc.2022.109851] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 10/02/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022]
Abstract
The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.
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Affiliation(s)
- Linh T. Duong
- Institute of Research and Development, Duy Tan University, Viet Nam
| | - Phuong T. Nguyen
- Department of Information Engineering, Computer Science and Mathematics University of L’Aquila, Italy,Corresponding author
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125
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Huang SC, Chaudhari AS, Langlotz CP, Shah N, Yeung S, Lungren MP. Developing medical imaging AI for emerging infectious diseases. Nat Commun 2022; 13:7060. [PMID: 36400764 PMCID: PMC9672573 DOI: 10.1038/s41467-022-34234-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/19/2022] [Indexed: 11/19/2022] Open
Abstract
Very few of the COVID-19 ML models were fit for deployment in real-world settings. In this Comment, Huang et al. discuss the main steps required to develop clinically useful models in the context of an emerging infectious disease.
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Affiliation(s)
- Shih-Cheng Huang
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Curtis P Langlotz
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Nigam Shah
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Serena Yeung
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthew P Lungren
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
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126
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Li Y, Xu C, Han J, An Z, Wang D, Ma H, Liu C. MHAU-Net: Skin Lesion Segmentation Based on Multi-Scale Hybrid Residual Attention Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:8701. [PMID: 36433298 PMCID: PMC9695536 DOI: 10.3390/s22228701] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Melanoma is a main factor that leads to skin cancer, and early diagnosis and treatment can significantly reduce the mortality of patients. Skin lesion boundary segmentation is a key to accurately localizing a lesion in dermoscopic images. However, the irregular shape and size of the lesions and the blurred boundary of the lesions pose significant challenges for researchers. In recent years, pixel-level semantic segmentation strategies based on convolutional neural networks have been widely used, but many methods still suffer from the inaccurate segmentation of fuzzy boundaries. In this paper, we proposed a multi-scale hybrid attentional convolutional neural network (MHAU-Net) for the precise localization and segmentation of skin lesions. MHAU-Net has four main components: multi-scale resolution input, hybrid residual attention (HRA), dilated convolution, and atrous spatial pyramid pooling. Multi-scale resolution inputs provide richer visual information, and HRA solves the problem of blurred boundaries and enhances the segmentation results. The Dice, mIoU, average specificity, and sensitivity on the ISIC2018 task 1 validation set were 93.69%, 90.02%, 92.7% and 93.9%, respectively. The segmentation metrics are significantly better than the latest DCSAU-Net, UNeXt, and U-Net, and excellent segmentation results are achieved on different datasets. We performed model robustness validations on the Kvasir-SEG dataset with an overall sensitivity and average specificity of 95.91% and 96.28%, respectively.
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Affiliation(s)
- Yingjie Li
- School of Integrated Circuits, Anhui University, Hefei 230601, China
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China
| | - Chao Xu
- School of Integrated Circuits, Anhui University, Hefei 230601, China
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China
| | - Jubao Han
- School of Integrated Circuits, Anhui University, Hefei 230601, China
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China
| | - Ziheng An
- School of Integrated Circuits, Anhui University, Hefei 230601, China
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China
| | - Deyu Wang
- School of Integrated Circuits, Anhui University, Hefei 230601, China
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China
| | - Haichao Ma
- School of Integrated Circuits, Anhui University, Hefei 230601, China
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China
| | - Chuanxu Liu
- School of Integrated Circuits, Anhui University, Hefei 230601, China
- Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China
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127
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Sun W, Pang Y, Zhang G. CCT: Lightweight compact convolutional transformer for lung disease CT image classification. Front Physiol 2022; 13:1066999. [DOI: 10.3389/fphys.2022.1066999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022] Open
Abstract
Computed tomography (CT) imaging results are an important criterion for the diagnosis of lung disease. CT images can clearly show the characteristics of lung lesions. Early and accurate detection of lung diseases helps clinicians to improve patient care effectively. Therefore, in this study, we used a lightweight compact convolutional transformer (CCT) to build a prediction model for lung disease classification using chest CT images. We added a position offset term and changed the attention mechanism of the transformer encoder to an axial attention mechanism module. As a result, the classification performance of the model was improved in terms of height and width. We show that the model effectively classifies COVID-19, community pneumonia, and normal conditions on the CC-CCII dataset. The proposed model outperforms other comparable models in the test set, achieving an accuracy of 98.5% and a sensitivity of 98.6%. The results show that our method achieves a larger field of perception on CT images, which positively affects the classification of CT images. Thus, the method can provide adequate assistance to clinicians.
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Roth HR, Xu Z, Tor-Díez C, Sanchez Jacob R, Zember J, Molto J, Li W, Xu S, Turkbey B, Turkbey E, Yang D, Harouni A, Rieke N, Hu S, Isensee F, Tang C, Yu Q, Sölter J, Zheng T, Liauchuk V, Zhou Z, Moltz JH, Oliveira B, Xia Y, Maier-Hein KH, Li Q, Husch A, Zhang L, Kovalev V, Kang L, Hering A, Vilaça JL, Flores M, Xu D, Wood B, Linguraru MG. Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge. Med Image Anal 2022; 82:102605. [PMID: 36156419 PMCID: PMC9444848 DOI: 10.1016/j.media.2022.102605] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 07/01/2022] [Accepted: 08/25/2022] [Indexed: 11/30/2022]
Abstract
Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.
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Affiliation(s)
- Holger R Roth
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany.
| | - Ziyue Xu
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany
| | - Carlos Tor-Díez
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, WA, DC, USA
| | - Ramon Sanchez Jacob
- Division of Diagnostic Imaging and Radiology, Children's National Hospital, WA,DC, USA
| | - Jonathan Zember
- Division of Diagnostic Imaging and Radiology, Children's National Hospital, WA,DC, USA
| | - Jose Molto
- Division of Diagnostic Imaging and Radiology, Children's National Hospital, WA,DC, USA
| | - Wenqi Li
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany
| | - Sheng Xu
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Baris Turkbey
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Dong Yang
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany
| | - Ahmed Harouni
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany
| | - Nicola Rieke
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany
| | - Shishuai Hu
- School of Computer Science and Engineering, Northwestern Polytechnical University, China
| | - Fabian Isensee
- Applied Computer Vision Lab, Helmholtz Imaging , Heidelberg, Germany; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Qinji Yu
- Shanghai Jiao Tong University, China
| | - Jan Sölter
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
| | - Tong Zheng
- School of Informatics, Nagoya University, Japan
| | - Vitali Liauchuk
- Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus
| | - Ziqi Zhou
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China
| | | | - Bruno Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Yong Xia
- School of Computer Science and Engineering, Northwestern Polytechnical University, China
| | - Klaus H Maier-Hein
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Qikai Li
- Shanghai Jiao Tong University, China
| | - Andreas Husch
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | | | - Vassili Kovalev
- Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus
| | - Li Kang
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China
| | - Alessa Hering
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Mona Flores
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany
| | - Daguang Xu
- NVIDIA, Bethesda, MD, USA; Santa Clara, CA, USA; Munich, Germany
| | - Bradford Wood
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, WA, DC, USA; School of Medicine and Health Sciences, George Washington University, WA, DC, USA
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129
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Muacevic A, Adler JR, Jones RH, Collins HR, Kabakus IM, McBee MP. COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence. Cureus 2022; 14:e31897. [PMID: 36579217 PMCID: PMC9792347 DOI: 10.7759/cureus.31897] [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] [Accepted: 11/22/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs. MATERIALS AND METHODS Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values. RESULTS A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78). CONCLUSION Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.
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130
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Jalali Moghaddam M, Ghavipour M. Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging. IPEM-TRANSLATION 2022; 3:100008. [PMID: 36312890 PMCID: PMC9597575 DOI: 10.1016/j.ipemt.2022.100008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/08/2022]
Abstract
The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.
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Affiliation(s)
- Marjan Jalali Moghaddam
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran
| | - Mina Ghavipour
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran
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131
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Velichko A, Huyut MT, Belyaev M, Izotov Y, Korzun D. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application. SENSORS (BASEL, SWITZERLAND) 2022; 22:7886. [PMID: 36298235 PMCID: PMC9610709 DOI: 10.3390/s22207886] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 05/16/2023]
Abstract
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
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Affiliation(s)
- Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Mehmet Tahir Huyut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000 Erzincan, Türkiye
| | - Maksim Belyaev
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Yuriy Izotov
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
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132
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Zhang S, Yuan GC. Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4509394. [PMID: 36285284 PMCID: PMC9588382 DOI: 10.1155/2022/4509394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/11/2022] [Accepted: 09/24/2022] [Indexed: 12/15/2022]
Abstract
Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.
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Affiliation(s)
- Sai Zhang
- Qualcomm Inc., 5775 Morehouse Drive, San Diego, CA 92121, USA
| | - Guo-Chang Yuan
- Department of Emergency Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
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Sinwar D, Dhaka VS, Tesfaye BA, Raghuwanshi G, Kumar A, Maakar SK, Agrawal S. Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:1306664. [PMID: 36304775 PMCID: PMC9581633 DOI: 10.1155/2022/1306664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/06/2022] [Accepted: 09/27/2022] [Indexed: 01/26/2023]
Abstract
Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.
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Affiliation(s)
- Deepak Sinwar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Biniyam Alemu Tesfaye
- Department of Computer Science, College of Informatics, Bule Hora University, Bule Hora, Ethiopia
| | - Ghanshyam Raghuwanshi
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Ashish Kumar
- Department of Mathematics and Statistics, Manipal University Jaipur, Jaipur, India
| | - Sunil Kr. Maakar
- School of Computing Science & Engineering, Galgotias University, Greater Noida, India
| | - Sanjay Agrawal
- Department of Electrical Engineering, Rajkiya Engineering College, Akbarpur, Ambedkar Nagar, India
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134
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Alyafei K, Ahmed R, Abir FF, Chowdhury MEH, Naji KK. A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices. Comput Biol Med 2022; 149:106070. [PMID: 36099862 PMCID: PMC9433350 DOI: 10.1016/j.compbiomed.2022.106070] [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: 02/04/2022] [Revised: 08/03/2022] [Accepted: 08/27/2022] [Indexed: 11/30/2022]
Abstract
Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.
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Affiliation(s)
- Khalid Alyafei
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar
| | - Rashid Ahmed
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar; Department of Biotechnology, Mirpur University of Science and Technology (MUST), Mirpur, 10250, AJK, Pakistan
| | - Farhan Fuad Abir
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
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135
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Punitha S, Al-Turjman F, Stephan T. A novel e-healthcare diagnosing system for COVID-19 via whale optimization algorithm. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2125079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- S. Punitha
- Department of Computer Science and Engineering, Graphics Era Deemed to be University, Dehradun, India
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department of AI and Robotics Institute, Near East University, Nicosia, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Kyrenia, Turkey
| | - Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, India
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136
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Liu XP, Yang X, Xiong M, Mao X, Jin X, Li Z, Zhou S, Chang H. Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening. Front Public Health 2022; 10:1004117. [PMID: 36211676 PMCID: PMC9533142 DOI: 10.3389/fpubh.2022.1004117] [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: 07/26/2022] [Accepted: 08/15/2022] [Indexed: 01/27/2023] Open
Abstract
Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.
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Affiliation(s)
- Xiao-Ping Liu
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xu Yang
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Miao Xiong
- Department of Radiology, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| | - Xuanyu Mao
- Department of Emergency, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoqing Jin
- Department of Emergency, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhiqiang Li
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shuang Zhou
- Hubei Province Hospital of Traditional Chinese Medicine, Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Hubei Institute of Traditional Chinese Medicine, Wuhan, China
| | - Hang Chang
- Department of Emergency, Zhongnan Hospital of Wuhan University, Wuhan, China
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137
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Integrated multimodal artificial intelligence framework for healthcare applications. NPJ Digit Med 2022; 5:149. [PMID: 36127417 PMCID: PMC9489871 DOI: 10.1038/s41746-022-00689-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 08/31/2022] [Indexed: 11/24/2022] Open
Abstract
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N = 34,537 samples) containing 7279 unique hospitalizations and 6485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6–33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48 h mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
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138
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Chen J, Li Y, Guo L, Zhou X, Zhu Y, He Q, Han H, Feng Q. Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review. Neural Comput Appl 2022; 36:1-19. [PMID: 36159188 PMCID: PMC9483435 DOI: 10.1007/s00521-022-07709-0] [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: 04/13/2022] [Accepted: 08/04/2022] [Indexed: 11/20/2022]
Abstract
Since 2020, novel coronavirus pneumonia has been spreading rapidly around the world, bringing tremendous pressure on medical diagnosis and treatment for hospitals. Medical imaging methods, such as computed tomography (CT), play a crucial role in diagnosing and treating COVID-19. A large number of CT images (with large volume) are produced during the CT-based medical diagnosis. In such a situation, the diagnostic judgement by human eyes on the thousands of CT images is inefficient and time-consuming. Recently, in order to improve diagnostic efficiency, the machine learning technology is being widely used in computer-aided diagnosis and treatment systems (i.e., CT Imaging) to help doctors perform accurate analysis and provide them with effective diagnostic decision support. In this paper, we comprehensively review these frequently used machine learning methods applied in the CT Imaging Diagnosis for the COVID-19, discuss the machine learning-based applications from the various kinds of aspects including the image acquisition and pre-processing, image segmentation, quantitative analysis and diagnosis, and disease follow-up and prognosis. Moreover, we also discuss the limitations of the up-to-date machine learning technology in the context of CT imaging computer-aided diagnosis.
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Affiliation(s)
- Jingjing Chen
- Zhejiang University City College, Hangzhou, China
- Zhijiang College of Zhejiang University of Technology, Shaoxing, China
| | - Yixiao Li
- Faculty of Science, Zhejiang University of Technology, Hangzhou, China
| | - Lingling Guo
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiaokang Zhou
- Faculty of Data Science, Shiga University, Hikone, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Yihan Zhu
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingfeng He
- School of Pharmacy, Fudan University, Shanghai, China
| | - Haijun Han
- School of Medicine, Zhejiang University City College, Hangzhou, China
| | - Qilong Feng
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
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139
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Liu J, Jiao X, Zeng S, Li H, Jin P, Chi J, Liu X, Yu Y, Ma G, Zhao Y, Li M, Peng Z, Huo Y, Gao QL. Oncological big data platforms for promoting digital competencies and professionalism in Chinese medical students: a cross-sectional study. BMJ Open 2022; 12:e061015. [PMID: 36109032 PMCID: PMC9478867 DOI: 10.1136/bmjopen-2022-061015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES Advancements in big data technology are reshaping the healthcare system in China. This study aims to explore the role of medical big data in promoting digital competencies and professionalism among Chinese medical students. DESIGN, SETTING AND PARTICIPANTS This study was conducted among 274 medical students who attended a workshop on medical big data conducted on 8 July 2021 in Tongji Hospital. The workshop was based on the first nationwide multifunction gynecologic oncology medical big data platform in China, at the National Union of Real-World Gynecologic Oncology Research & Patient Management Platform (NUWA platform). OUTCOME MEASURES Data on knowledge, attitudes towards big data technology and professionalism were collected before and after the workshop. We have measured the four skill categories: doctor‒patient relationship skills, reflective skills, time management and interprofessional relationship skills using the Professionalism Mini-Evaluation Exercise (P-MEX) as a reflection for professionalism. RESULTS A total of 274 students participated in this workshop and completed all the surveys. Before the workshop, only 27% of them knew the detailed content of medical big data platforms, and 64% knew the potential application of medical big data. The majority of the students believed that big data technology is practical in their clinical practice (77%), medical education (85%) and scientific research (82%). Over 80% of the participants showed positive attitudes toward big data platforms. They also exhibited sufficient professionalism before the workshop. Meanwhile, the workshop significantly promoted students' knowledge of medical big data (p<0.05), and led to more positive attitudes towards big data platforms and higher levels of professionalism. CONCLUSIONS Chinese medical students have primitive acquaintance and positive attitudes toward big data technology. The NUWA platform-based workshop may potentially promote their understanding of big data and enhance professionalism, according to the self-measured P-MEX scale.
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Affiliation(s)
- Jiahao Liu
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaofei Jiao
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shaoqing Zeng
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huayi Li
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ping Jin
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jianhua Chi
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xingyu Liu
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yang Yu
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guanchen Ma
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yingjun Zhao
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ming Li
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zikun Peng
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yabing Huo
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qing-Lei Gao
- Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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140
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Futterman ID, McLaren R, Friedmann H, Musleh N, Haberman S. Use of Machine Learning to Identify Clinical Variables in Pregnant and Non-pregnant Women with SARS-CoV-2 Infection. Methods Inf Med 2022; 61:61-67. [PMID: 36096142 DOI: 10.1055/s-0042-1756282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
OBJECTIVE The aim of the study is to identify the important clinical variables found in both pregnant and non-pregnant women who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, using an artificial intelligence (AI) platform. MATERIALS AND METHODS This was a retrospective cohort study of all women between the ages of 18 to 45, who were admitted to Maimonides Medical Center between March 10, 2020 and December 20, 2021. Patients were included if they had nasopharyngeal PCR swab positive for SARS-CoV-2. Safe People Artificial Intelligence (SPAI) platform, developed by Gynisus, Inc., was used to identify key clinical variables predicting a positive test in pregnant and non-pregnant women. A list of mathematically important clinical variables was generated for both non-pregnant and pregnant women. RESULTS Positive results were obtained in 1,935 non-pregnant women and 1,909 non-pregnant women tested negative for SARS-CoV-2 infection. Among pregnant women, 280 tested positive, and 1,000 tested negative. The most important clinical variable to predict a positive swab result in non-pregnant women was age, while elevated D-dimer levels and presence of an abnormal fetal heart rate pattern were the most important clinical variable in pregnant women to predict a positive test. CONCLUSION In an attempt to better understand the natural history of the SARS-CoV-2 infection we present a side-by-side analysis of clinical variables found in pregnant and non-pregnant women who tested positive for COVID-19. These clinical variables can help stratify and highlight those at risk for SARS-CoV-2 infection and shed light on the individual patient risk for testing positive.
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Affiliation(s)
- Itamar D Futterman
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Maimonides Medical Center, Brooklyn, New York
| | - Rodney McLaren
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Maimonides Medical Center, Brooklyn, New York.,Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Thomas Jefferson University Hospital - Jefferson Health, Philadelphia, Pennsylvania
| | | | | | - Shoshana Haberman
- Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Maimonides Medical Center, Brooklyn, New York
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141
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Cheng J, Zhao W, Liu J, Xie X, Wu S, Liu L, Yue H, Li J, Wang J, Liu J. Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2723-2736. [PMID: 34351863 PMCID: PMC9647725 DOI: 10.1109/tcbb.2021.3102584] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19.
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142
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Gupta S, Shabaz M, Vyas S. Artificial intelligence and IoT based prediction of Covid-19 using chest X-ray images. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2022; 25:100299. [PMID: 35783463 PMCID: PMC9233885 DOI: 10.1016/j.smhl.2022.100299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/31/2022] [Accepted: 06/15/2022] [Indexed: 11/30/2022]
Abstract
Coronavirus illness (COVID-19), discovered in late 2019, has spread rapidly worldwide, resulting in significant mortality. This study analyzed the performance of studies that employed machines and DL on chest X-ray pictures and CT scans for COVID-19 diagnosis. ML approaches on CT and X-ray images aided incorrectly in identifying COVID-19. The fast spread of COVID-19 worldwide and the growing number of deaths necessitates an immediate response from all sectors. Authorities will be able to deal with the effects more efficiently if such illnesses can be predicted in the future. Furthermore, it is crucial to maintain track of the number of infected persons through regular check-ups, and it is frequently required to confine affected people and implement medical treatments. In addition, various additional elements, such as environmental influences and commonalities among the most afflicted places, should be considered to slow the spread of COVID-19, and precautions should be taken. AI-based approaches for the prediction and diagnosis of COVID-19 were suggested in this paper. This Review Article discusses current advances in AI technology and its biological applications, particularly the coronavirus.
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Affiliation(s)
- Surbhi Gupta
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India
| | - Sonali Vyas
- University of Petroleum and Energy Studies, Dehradun, India
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143
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Sun W, Chen J, Yan L, Lin J, Pang Y, Zhang G. COVID-19 CT image segmentation method based on swin transformer. Front Physiol 2022; 13:981463. [PMID: 36072854 PMCID: PMC9441795 DOI: 10.3389/fphys.2022.981463] [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: 06/29/2022] [Accepted: 07/11/2022] [Indexed: 12/12/2022] Open
Abstract
Owing to its significant contagion and mutation, the new crown pneumonia epidemic has caused more than 520 million infections worldwide and has brought irreversible effects on the society. Computed tomography (CT) images can clearly demonstrate lung lesions of patients. This study used deep learning techniques to assist doctors in the screening and quantitative analysis of this disease. Consequently, this study will help to improve the diagnostic efficiency and reduce the risk of infection. In this study, we propose a new method to improve U-Net for lesion segmentation in the chest CT images of COVID-19 patients. 750 annotated chest CT images of 150 patients diagnosed with COVID-19 were selected to classify, identify, and segment the background area, lung area, ground glass opacity, and lung parenchyma. First, to address the problem of a loss of lesion detail during down sampling, we replaced part of the convolution operation with atrous convolution in the encoder structure of the segmentation network and employed convolutional block attention module (CBAM) to enhance the weighting of important feature information. Second, the Swin Transformer structure is introduced in the last layer of the encoder to reduce the number of parameters and improve network performance. We used the CC-CCII lesion segmentation dataset for training and validation of the model effectiveness. The results of ablation experiments demonstrate that this method achieved significant performance gain, in which the mean pixel accuracy is 87.62%, mean intersection over union is 80.6%, and dice similarity coefficient is 88.27%. Further, we verified that this model achieved superior performance in comparison to other models. Thus, the method proposed herein can better assist doctors in evaluating and analyzing the condition of COVID-19 patients.
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Affiliation(s)
- Weiwei Sun
- Chongqing University of Posts and Telecommunication, Chongqing, China
| | - Jungang Chen
- Chongqing University of Posts and Telecommunication, Chongqing, China
| | - Li Yan
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Jinzhao Lin
- Chongqing University of Posts and Telecommunication, Chongqing, China
| | - Yu Pang
- Chongqing University of Posts and Telecommunication, Chongqing, China
- *Correspondence: Yu Pang, ; Guo Zhang,
| | - Guo Zhang
- Chongqing University of Posts and Telecommunication, Chongqing, China
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
- *Correspondence: Yu Pang, ; Guo Zhang,
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144
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Yousefzadeh M, Hasanpour M, Zolghadri M, Salimi F, Yektaeian Vaziri A, Mahmoudi Aqeel Abadi A, Jafari R, Esfahanian P, Nazem-Zadeh MR. Deep learning framework for prediction of infection severity of COVID-19. Front Med (Lausanne) 2022; 9:940960. [PMID: 36059818 PMCID: PMC9428758 DOI: 10.3389/fmed.2022.940960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained SE-ResNet18 based U-Net models, one for each of the axial, coronal, and sagittal views. By having the lobe and infection segmentation masks, we calculate the infection severity percentage in each lobe and classify that percentage into 6 categories of infection severity score using a k-nearest neighbors (k-NN) model. The lobe segmentation model achieved a Dice Similarity Score (DSC) in the range of [0.918, 0.981] for different lung lobes and our infection segmentation models gained DSC scores of 0.7254 and 0.7105 on our two test sets, respectfully. Similarly, two resident radiologists were assigned the same infection segmentation tasks, for which they obtained a DSC score of 0.7281 and 0.6693 on the two test sets. At last, performance on infection severity score over the entire test datasets was calculated, for which the framework's resulted in a Mean Absolute Error (MAE) of 0.505 ± 0.029, while the resident radiologists' was 0.571 ± 0.039.
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Affiliation(s)
- Mehdi Yousefzadeh
- Department of Physics, Shahid Beheshti University, Tehran, Iran
- School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Masoud Hasanpour
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Mozhdeh Zolghadri
- Department of Medical Physics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Salimi
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Ava Yektaeian Vaziri
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Abolfazl Mahmoudi Aqeel Abadi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ramezan Jafari
- Department of Radiology, Health Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Parsa Esfahanian
- School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad-Reza Nazem-Zadeh
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
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145
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Ahmad M, Ahmed I, Jeon G. A sustainable advanced artificial intelligence-based framework for analysis of COVID-19 spread. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2022:1-16. [PMID: 35993085 PMCID: PMC9379242 DOI: 10.1007/s10668-022-02584-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 04/21/2022] [Indexed: 06/15/2023]
Abstract
The idea of sustainability aims to provide a protected operating environment that supports without risking the capacity of coming generations and to satisfy their demands in the future. With the advent of artificial intelligence, big data, and the Internet of Things, there is a tremendous paradigm transformation in how environmental data are managed and handled for sustainable applications in smart cities and societies. The ongoing COVID-19 (Coronavirus Disease) pandemic maintains a mortifying impact on the world population's health. A continuous rise in the number of positive cases produced much stress on governing organizations worldwide, and they are finding it challenging to handle the situation. Artificial Intelligence methods can be extended quite efficiently to monitor the disease, predict the pandemic's growth, and outline policies and strategies to control its transmission or spread. The combination of healthcare, along with big data, and machine learning methods, can improve the quality of life by providing better care services and creating cost-effective systems. Researchers have been using these techniques to fight against the COVID-19 pandemic. This paper emphasizes on the analysis of different factors and symptoms and presents a sustainable framework to predict and detect COVID-19. Firstly, we have collected a data set having different symptoms information of COVID-19. Then, we have explored various machine learning algorithms or methods: including Logistic Regression, Naive Bayes, Decision Tree, Random Forest Classifier, Extreme Gradient Boost, K-Nearest Neighbour, and Support Vector Machine to predict and detect COVID-19 lab results, using different symptoms information. The model might help to predict and detect the long-term spread of a pandemic and implement advanced proactive measures. The findings show that the Logistic Regression and Support Vector Machine outperformed from other machine learning algorithms in terms of accuracy; algorithms exhibit 97.66% and 98% results, respectively.
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Affiliation(s)
- Misbah Ahmad
- Center of Excellence in Information Technology, Institute of Management Sciences, 1-A, Sector E-5, Phase VII, Peshawar, Hayatabad Pakistan
| | - Imran Ahmed
- School of Computing and Information Science, Anglia Ruskin University, Cambridge East Road, Cambridge, CB1 1PT UK
| | - Gwanggil Jeon
- Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea
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146
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Anta JA, Martínez-Ballestero I, Eiroa D, García J, Rodríguez-Comas J. Artificial intelligence for the detection of pancreatic lesions. Int J Comput Assist Radiol Surg 2022; 17:1855-1865. [PMID: 35951286 DOI: 10.1007/s11548-022-02706-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/17/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE Pancreatic cancer is one of the most lethal neoplasms among common cancers worldwide, and PCLs are well-known precursors of this type of cancer. Artificial intelligence (AI) could help to improve and speed up the detection and classification of pancreatic lesions. The aim of this review is to summarize the articles addressing the diagnostic yield of artificial intelligence applied to medical imaging (computed tomography [CT] and/or magnetic resonance [MR]) for the detection of pancreatic cancer and pancreatic cystic lesions. METHODS We performed a comprehensive literature search using PubMed, EMBASE, and Scopus (from January 2010 to April 2021) to identify full articles evaluating the diagnostic accuracy of AI-based methods processing CT or MR images to detect pancreatic ductal adenocarcinoma (PDAC) or pancreatic cystic lesions (PCLs). RESULTS We found 20 studies meeting our inclusion criteria. Most of the AI-based systems used were convolutional neural networks. Ten studies addressed the use of AI to detect PDAC, eight studies aimed to detect and classify PCLs, and 4 aimed to predict the presence of high-grade dysplasia or cancer. CONCLUSION AI techniques have shown to be a promising tool which is expected to be helpful for most radiologists' tasks. However, methodologic concerns must be addressed, and prospective clinical studies should be carried out before implementation in clinical practice.
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Affiliation(s)
- Julia Arribas Anta
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Gastroenterology, University Hospital, 12 Octubre. Av. de Córdoba, s/n, 28041, Madrid, Spain
| | - Iván Martínez-Ballestero
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Daniel Eiroa
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Javier García
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Júlia Rodríguez-Comas
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.
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147
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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148
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Carbonell G, Del Valle DM, Gonzalez-Kozlova E, Marinelli B, Klein E, El Homsi M, Stocker D, Chung M, Bernheim A, Simons NW, Xiang J, Nirenberg S, Kovatch P, Lewis S, Merad M, Gnjatic S, Taouli B. Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients. Heliyon 2022; 8:e10166. [PMID: 35958514 PMCID: PMC9356575 DOI: 10.1016/j.heliyon.2022.e10166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 03/08/2022] [Accepted: 07/27/2022] [Indexed: 01/29/2023] Open
Abstract
Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.
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Affiliation(s)
- Guillermo Carbonell
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Universidad de Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria, Spain
| | - Diane Marie Del Valle
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Gonzalez-Kozlova
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett Marinelli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Emma Klein
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria El Homsi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Michael Chung
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adam Bernheim
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole W. Simons
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jiani Xiang
- Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sharon Nirenberg
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia Kovatch
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Lewis
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miriam Merad
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Oncological Sciences; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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149
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Pease M, Arefan D, Barber J, Yuh E, Puccio A, Hochberger K, Nwachuku E, Roy S, Casillo S, Temkin N, Okonkwo DO, Wu S. Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans. Radiology 2022; 304:385-394. [PMID: 35471108 PMCID: PMC9340242 DOI: 10.1148/radiol.212181] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/29/2022] [Accepted: 02/23/2022] [Indexed: 12/23/2022]
Abstract
Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep learning of head CT scans and clinical information to predict long-term outcomes after sTBI. Materials and Methods This was a retrospective analysis of two prospectively collected databases. The model-building set included 537 patients (mean age, 40 years ± 17 [SD]; 422 men) from one institution from November 2002 to December 2018. Transfer learning and curriculum learning were applied to a convolutional neural network using admission head CT to predict mortality and unfavorable outcomes (Glasgow Outcomes Scale scores 1-3) at 6 months. This was combined with clinical input for a holistic fusion model. The models were evaluated using an independent internal test set and an external cohort of 220 patients with sTBI (mean age, 39 years ± 17; 166 men) from 18 institutions in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study from February 2014 to April 2018. The models were compared with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model and the predictions of three neurosurgeons. Area under the receiver operating characteristic curve (AUC) was used as the main model performance metric. Results The fusion model had higher AUCs than did the IMPACT model in the prediction of mortality (AUC, 0.92 [95% CI: 0.86, 0.97] vs 0.80 [95% CI: 0.71, 0.88]; P < .001) and unfavorable outcomes (AUC, 0.88 [95% CI: 0.82, 0.94] vs 0.82 [95% CI: 0.75, 0.90]; P = .04) on the internal data set. For external TRACK-TBI testing, there was no evidence of a significant difference in the performance of any models compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.90) in the prediction of mortality. The Imaging model (AUC, 0.73; 95% CI: 0.66-0.81; P = .02) and the fusion model (AUC, 0.68; 95% CI: 0.60, 0.76; P = .02) underperformed as compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.89) in the prediction of unfavorable outcomes. The fusion model outperformed the predictions of the neurosurgeons. Conclusion A deep learning model of head CT and clinical information can be used to predict 6-month outcomes after severe traumatic brain injury. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Haller in this issue.
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Affiliation(s)
| | | | - Jason Barber
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | - Esther Yuh
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | - Ava Puccio
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | - Kerri Hochberger
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | - Enyinna Nwachuku
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | - Souvik Roy
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | - Stephanie Casillo
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | - Nancy Temkin
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
| | | | | | - on behalf of TRACK-TBI Investigators
- From the Department of Neurosurgery, University of Pittsburgh Medical
Center, Pittsburgh, Pa (M.P., A.P., K.H., E.N., S.R., S.C., D.O.O.); Departments
of Radiology (D.A., S.W.), Biomedical Informatics (S.W.), and Bioengineering
(S.W.), and Intelligent Systems Program (S.W.), University of Pittsburgh, 3240
Craft Pl, Room 322, Pittsburgh, PA 15213; Department of Neurosurgery, University
of Washington, Seattle, Wash (J.B., N.T.); Department of Radiology, University
of California San Francisco, San Francisco, Calif (E.Y.)
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150
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Joshi AM, Nayak DR, Das D, Zhang YD. LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3551647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Recent years have witnessed a rise in employing deep learning methods, especially convlolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called as LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model.
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Affiliation(s)
- Amogh Manoj Joshi
- Vivekanand Education Society’s Institute of Technology, Mumbai, India
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