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Liontos A, Biros D, Matzaras R, Tsarapatsani KH, Kolios NG, Zarachi A, Tatsis K, Pappa C, Nasiou M, Pargana E, Tsiakas I, Lymperatou D, Filippas-Ntekouan S, Athanasiou L, Samanidou V, Konstantopoulou R, Vagias I, Panteli A, Milionis H, Christaki E. Inflammation and Venous Thromboembolism in Hospitalized Patients with COVID-19. Diagnostics (Basel) 2023; 13:3477. [PMID: 37998613 PMCID: PMC10670045 DOI: 10.3390/diagnostics13223477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/04/2023] [Accepted: 11/17/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND A link between inflammation and venous thromboembolism (VTE) in COVID-19 disease has been suggested pathophysiologically and clinically. The aim of this study was to investigate the association between inflammation and disease outcomes in adult hospitalized COVID-19 patients with VTE. METHODS This was a retrospective observational study, including quantitative and qualitative data collected from COVID-19 patients hospitalized at the Infectious Diseases Unit (IDU) of the University Hospital of Ioannina, from 1 March 2020 to 31 May 2022. Venous thromboembolism was defined as a diagnosis of pulmonary embolism (PE) and/or vascular tree-in-bud in the lungs. The burden of disease, assessed by computed tomography of the lungs (CTBoD), was quantified as the percentage (%) of the affected lung parenchyma. The study outcomes were defined as death, intubation, and length of hospital stay (LoS). A chi-squared test and univariate logistic regression analyses were performed in IBM SPSS 28.0. RESULTS After propensity score matching, the final study cohort included 532 patients. VTE was found in 11.2% of the total population. In patients with VTE, we found that lymphocytopenia and a high neutrophil/lymphocyte ratio were associated with an increased risk of intubation and death, respectively. Similarly, CTBoD > 50% was associated with a higher risk of intubation and death in this group of patients. The triglyceride-glucose (TyG) index was also linked to worse outcomes. CONCLUSIONS Inflammatory indices were associated with VTE. Lymphocytopenia and an increased neutrophil-to-lymphocyte ratio negatively impacted the disease's prognosis and outcomes. Whether these indices unfavorably affect outcomes in COVID-19-associated VTE must be further evaluated.
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Affiliation(s)
- Angelos Liontos
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Dimitrios Biros
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Rafail Matzaras
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | | | - Nikolaos-Gavriel Kolios
- Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (N.-G.K.); (C.P.); (M.N.); (E.P.)
| | - Athina Zarachi
- Department of Otorhinolaryngology, Head and Neck Surgery, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 451100 Ioannina, Greece;
| | - Konstantinos Tatsis
- Department of Respiratory Medicine, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 451100 Ioannina, Greece;
| | - Christiana Pappa
- Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (N.-G.K.); (C.P.); (M.N.); (E.P.)
| | - Maria Nasiou
- Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (N.-G.K.); (C.P.); (M.N.); (E.P.)
| | - Eleni Pargana
- Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (N.-G.K.); (C.P.); (M.N.); (E.P.)
| | - Ilias Tsiakas
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Diamantina Lymperatou
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Sempastien Filippas-Ntekouan
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Lazaros Athanasiou
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Valentini Samanidou
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Revekka Konstantopoulou
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Ioannis Vagias
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Aikaterini Panteli
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Haralampos Milionis
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
| | - Eirini Christaki
- 1st Division of Internal Medicine & Infectious Diseases Unit, University General Hospital of Ioannina, Faculty of Medicine, University of Ioannina, 45500 Ioannina, Greece; (A.L.); (D.B.); (R.M.); (I.T.); (D.L.); (S.F.-N.); (L.A.); (V.S.); (R.K.); (I.V.); (A.P.); (H.M.)
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Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics (Basel) 2023; 13:1749. [PMID: 37238232 PMCID: PMC10217633 DOI: 10.3390/diagnostics13101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Affiliation(s)
| | - Murat Koyuncu
- Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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Cabrera Alvargonzález J, Larrañaga Janeiro A, Pérez Castro S, Martínez Torres J, Martínez Lamas L, Daviña Nuñez C, Del Campo-Pérez V, Suarez Luque S, Regueiro García B, Porteiro Fresco J. Proof of concept of the potential of a machine learning algorithm to extract new information from conventional SARS-CoV-2 rRT-PCR results. Sci Rep 2023; 13:7786. [PMID: 37179356 PMCID: PMC10182547 DOI: 10.1038/s41598-023-34882-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/09/2023] [Indexed: 05/15/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges modern society has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Overall, this study suggests that there is valuable residual information in the rRT-PCR positive samples that can be used to identify patterns in the development of the SARS-CoV-2 pandemic. The successful application of supervised classification algorithms to detect these patterns demonstrates the potential of machine learning techniques to aid in understanding the spread of the virus and its variants.
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Affiliation(s)
- Jorge Cabrera Alvargonzález
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
- Universidade de Vigo, Vigo, Spain
| | | | - Sonia Pérez Castro
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
- Universidade de Vigo, Vigo, Spain
| | - Javier Martínez Torres
- Applied Mathematics I, Telecommunications Engineering School, Universidad de Vigo, 36310, Vigo, Spain
| | - Lucía Martínez Lamas
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
| | - Carlos Daviña Nuñez
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Víctor Del Campo-Pérez
- Department of Preventive Medicine and Public Health, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
| | - Silvia Suarez Luque
- Dirección Xeral de Saúde Pública, Consellería de Sanidade, Xunta de Galicia, Santiago de Compostela, A Coruña, Spain
| | - Benito Regueiro García
- Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo, Spain
- Microbiology and Parasitology Department, Medicine and Odontology, Universidade de Santiago, Santiago de Compostela, Spain
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Hayat A, Baglat P, Mendonça F, Mostafa SS, Morgado-Dias F. Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1268. [PMID: 36674023 PMCID: PMC9858730 DOI: 10.3390/ijerph20021268] [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: 11/21/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.
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Affiliation(s)
- Ahatsham Hayat
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Preety Baglat
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Fábio Mendonça
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | | | - Fernando Morgado-Dias
- University of Madeira, 9000-082 Funchal, Portugal
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
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5
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Sidibé ML, Yonaba R, Tazen F, Karoui H, Koanda O, Lèye B, Andrianisa HA, Karambiri H. Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2022; 25:1-29. [PMID: 36061268 PMCID: PMC9424840 DOI: 10.1007/s10668-022-02646-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s10668-022-02646-3.
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Affiliation(s)
- Mohamed Lamine Sidibé
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
| | - Roland Yonaba
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
| | - Fowé Tazen
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
| | - Héla Karoui
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
| | - Ousmane Koanda
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
| | - Babacar Lèye
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
| | - Harinaivo Anderson Andrianisa
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
| | - Harouna Karambiri
- Laboratoire Eaux, Hydro-Systèmes Et Agriculture (LEHSA), Institut International d’Ingénierie de l’Eau Et de l’Environnement (2iE), 1 Rue de la Science, 01 BP 594, Ouagadougou 01, Burkina Faso
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Matysek A, Studnicka A, Smith WM, Hutny M, Gajewski P, Filipiak KJ, Goh J, Yang G. Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population. Front Med (Lausanne) 2022; 9:962101. [PMID: 35979209 PMCID: PMC9377050 DOI: 10.3389/fmed.2022.962101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection. Methods Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score. Results The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count. Conclusion The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.
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Affiliation(s)
- Adrian Matysek
- Immunidex Ltd., London, United Kingdom
- Cognescence Ltd., London, United Kingdom
| | - Aneta Studnicka
- Clinical Analysis Laboratory, Silesian Centre for Heart Diseases, Zabrze, Poland
| | - Wade Menpes Smith
- Immunidex Ltd., London, United Kingdom
- Cognescence Ltd., London, United Kingdom
| | - Michał Hutny
- Faculty of Medical Sciences in Katowice, Students’ Scientific Society, Medical University of Silesia, Katowice, Poland
| | - Paweł Gajewski
- AGH University of Science and Technology, Krakow, Poland
| | | | - Jorming Goh
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- National University Health System (NUHS), Centre for Healthy Longevity, Singapore, Singapore
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning. Interdiscip Sci 2022; 14:452-470. [PMID: 35133633 PMCID: PMC8846962 DOI: 10.1007/s12539-021-00499-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 12/18/2022]
Abstract
Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had a significant influence on both economy and health infrastructure worldwide. This novel virus is diagnosed utilising a conventional method known as the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. This approach, however, produces a lot of false-negative and erroneous outcomes. According to recent studies, COVID-19 can also be diagnosed using X-rays, CT scans, blood tests and cough sounds. In this article, we use blood tests and machine learning to predict the diagnosis of this deadly virus. We also present an extensive review of various existing machine-learning applications that diagnose COVID-19 from clinical and laboratory markers. Four different classifiers along with a technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) method was utilized to calculate the gravity of each feature and it was found that eosinophils, monocytes, leukocytes and platelets were the most critical blood parameters that distinguished COVID-19 infection for our dataset. These classifiers can be utilized in conjunction with RT-PCR tests to improve sensitivity and in emergency situations such as a pandemic outbreak that might happen due to new strains of the virus. The positive results indicate the prospective use of an automated framework that could help clinicians and medical personnel diagnose and screen patients.
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Cocoş R, Mahler B, Turcu-Stiolica A, Stoichiță A, Ghinet A, Shelby ES, Bohîlțea LC. Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study. Viruses 2022; 14:v14061201. [PMID: 35746672 PMCID: PMC9228480 DOI: 10.3390/v14061201] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 12/21/2022] Open
Abstract
Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79–0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81–0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.
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Affiliation(s)
- Relu Cocoş
- Institute of Pneumophtisiology “Marius Nasta”, 050159 Bucharest, Romania; (B.M.); (A.S.); (A.G.)
- Department of Medical Genetics, University of Medicine and Pharmacy “Carol Davila”, 020032 Bucharest, Romania;
- Correspondence: (R.C.); (A.T.-S.)
| | - Beatrice Mahler
- Institute of Pneumophtisiology “Marius Nasta”, 050159 Bucharest, Romania; (B.M.); (A.S.); (A.G.)
- Pneumology Department (II), University of Medicine and Pharmacy “Carol Davila”, 020021 Bucharest, Romania
| | - Adina Turcu-Stiolica
- Department of Pharmacoeconomics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
- Correspondence: (R.C.); (A.T.-S.)
| | - Alexandru Stoichiță
- Institute of Pneumophtisiology “Marius Nasta”, 050159 Bucharest, Romania; (B.M.); (A.S.); (A.G.)
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 020021 Bucharest, Romania
| | - Andreea Ghinet
- Institute of Pneumophtisiology “Marius Nasta”, 050159 Bucharest, Romania; (B.M.); (A.S.); (A.G.)
| | - Elena-Silvia Shelby
- Scientific Research Nucleus, Dr. Nicolae Robanescu National Clinical Centre for Children’s Neurorecovery, 041408 Bucharest, Romania;
| | - Laurențiu Camil Bohîlțea
- Department of Medical Genetics, University of Medicine and Pharmacy “Carol Davila”, 020032 Bucharest, Romania;
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9
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Thimoteo LM, Vellasco MM, Amaral J, Figueiredo K, Yokoyama CL, Marques E. Explainable Artificial Intelligence for COVID-19 Diagnosis Through Blood Test Variables. JOURNAL OF CONTROL, AUTOMATION AND ELECTRICAL SYSTEMS 2022; 33. [PMCID: PMC8722647 DOI: 10.1007/s40313-021-00858-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
This work proposes an explainable artificial intelligence approach to help diagnose COVID-19 patients based on blood test and pathogen variables. Two glass-box models, logistic regression and explainable boosting machine, and two black-box models, random forest and support vector machine, were used to assess the disease diagnosis. Shapley additive explanations were used to explain predictions for the black-box models, while glass-box models feature importance brought insights into the most relevant features. All global explanations show the eosinophils and leukocytes, white blood cells are among the essential features to help diagnose the COVID-19. Moreover, the best model obtained an AUC of 0.87.
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Affiliation(s)
- Lucas M. Thimoteo
- Departamento de Engenharia Elétrica, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Marley M. Vellasco
- Departamento de Engenharia Elétrica, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Jorge Amaral
- Programa de Pós-Graduação em Engenharia Eletrônica (PEL), Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Karla Figueiredo
- Programa de Pós-Graduação em Ciências Computacionais (CCOMP), Programa de Pós-Graduação em Telessaúde, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ Brasil
| | - Cátia Lie Yokoyama
- Departamento de Biologia Geral, Universidade Estadual de Londrina, Londrina, PR Brasil
| | - Erito Marques
- Programa de Pós-Graduação em Engenharia Eletrônica (PEL), Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ Brasil
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10
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Cushnan D, Bennett O, Berka R, Bertolli O, Chopra A, Dorgham S, Favaro A, Ganepola T, Halling-Brown M, Imreh G, Jacob J, Jefferson E, Lemarchand F, Schofield D, Wyatt JC. An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis. Gigascience 2021; 10:giab076. [PMID: 34849869 PMCID: PMC8633457 DOI: 10.1093/gigascience/giab076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 08/04/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19-affected UK population in terms of geographic, demographic, and temporal coverage. FINDINGS The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. CONCLUSION The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.
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Affiliation(s)
- Dominic Cushnan
- AI Lab, NHSX, Skipton House, 80 London Road, London SE1 6LH,
UK
| | | | | | | | | | | | | | | | - Mark Halling-Brown
- Scientific Computing, Royal Surrey NHS Foundation Trust,
Egerton Road, Guildford GU2 7XX, UK
| | | | - Joseph Jacob
- UCL Respiratory, 1st Floor, Rayne Institute, University College
London, London WC1E 6JF, UK
| | - Emily Jefferson
- Health Data Research UK, Gibbs Building, 215 Euston Road,
London NW1 2BE, UK
- Health Informatics Centre (HIC), School of Medicine, University of
Dundee, DD1 4HN, Dundee, UK
| | | | | | - Jeremy C Wyatt
- Emeritus Professor of Digital Healthcare, University of
Southampton, Southampton SO17 1BJ, UK
- NHSX, Skipton House, 80 London Road, London SE1 6LH, UK
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11
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Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021; 11:diagnostics11101924. [PMID: 34679622 PMCID: PMC8534829 DOI: 10.3390/diagnostics11101924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.
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Affiliation(s)
- Tianming Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Quanliang Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Cong Ma
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Xiangyu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Molecular Imaging Research Center, Central South University, Changsha 410008, China
- Correspondence:
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12
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Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, Valdesi C, Croce P, Mastrodicasa D, Villani M, Trebeschi S, Serafini FL, Rosa C, Cocco G, Luberti R, Conte S, Mazzamurro L, Mereu M, Patea RL, Panara V, Marinari S, Vecchiet J, Caulo M. Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease. Sci Rep 2021; 11:17237. [PMID: 34446812 PMCID: PMC8390673 DOI: 10.1038/s41598-021-96755-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 08/09/2021] [Indexed: 12/31/2022] Open
Abstract
Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10-7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.
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Affiliation(s)
- Andrea Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
| | - Piero Chiacchiaretta
- Center of Advanced Studies and Technology (CAST), "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
- Department of Psychological, Health and Territory Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | - Cristina Valdesi
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
| | | | - Michela Villani
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Consuelo Rosa
- Department of Radiation Oncology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via Dei Vestini, 66100, Chieti, Italy
| | - Giulio Cocco
- Unit of Ultrasound in Internal Medicine, Department of Medicine and Science of Aging, "G. D'Annunzio" University, Chieti, Italy
| | - Riccardo Luberti
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Sabrina Conte
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Lucia Mazzamurro
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Manuela Mereu
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Rosa Lucia Patea
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Valentina Panara
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
| | - Stefano Marinari
- Department of Pneumology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via Dei Vestini, 66100, Chieti, Italy
| | - Jacopo Vecchiet
- Clinic of Infectious Diseases, Department of Medicine and Science of Aging, University 'G. d'Annunzio' Chieti-Pescara, 66100, Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University, Chieti, Italy
- Department of Radiology, "Santissima Annunziata" Hospital, "G. d'Annunzio" University of Chieti, Via dei Vestini, 66100, Chieti, Italy
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13
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Du R, Tsougenis ED, Ho JWK, Chan JKY, Chiu KWH, Fang BXH, Ng MY, Leung ST, Lo CSY, Wong HYF, Lam HYS, Chiu LFJ, So TY, Wong KT, Wong YCI, Yu K, Yeung YC, Chik T, Pang JWK, Wai AKC, Kuo MD, Lam TPW, Khong PL, Cheung NT, Vardhanabhuti V. Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph. Sci Rep 2021; 11:14250. [PMID: 34244563 PMCID: PMC8270945 DOI: 10.1038/s41598-021-93719-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 06/21/2021] [Indexed: 01/08/2023] Open
Abstract
Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.
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Affiliation(s)
- Richard Du
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Artificial Intelligence Lab, Head Office Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Efstratios D Tsougenis
- Artificial Intelligence Lab, Head Office Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Joshua W K Ho
- The School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Joyce K Y Chan
- Clinical Systems, Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Keith W H Chiu
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | | | - Ming Yen Ng
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Siu-Ting Leung
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, SAR, China
| | - Christine S Y Lo
- Department of Radiology, Hong Kong Sanatorium & Hospital, Hong Kong, SAR, China
| | - Ho-Yuen F Wong
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Hiu-Yin S Lam
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Long-Fung J Chiu
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, SAR, China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ka Tak Wong
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Hong Kong, SAR, China
| | - Yiu Chung I Wong
- Department of Radiology, Tuen Muen Hospital, Hong Kong, SAR, China
| | - Kevin Yu
- Department of Radiology, Tuen Muen Hospital, Hong Kong, SAR, China
| | - Yiu-Cheong Yeung
- Department of Medicine, Princess Margaret Hospital, Hong Kong, SAR, China
| | - Thomas Chik
- Department of Medicine, Princess Margaret Hospital, Hong Kong, SAR, China
| | - Joanna W K Pang
- Health Informatics, Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Abraham Ka-Chung Wai
- Emergency Medicine Unit, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Michael D Kuo
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Tina P W Lam
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Ngai-Tseung Cheung
- Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
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