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Djahnine A, Jupin-Delevaux E, Nempont O, Si-Mohamed SA, Craighero F, Cottin V, Douek P, Popoff A, Boussel L. Weakly-supervised learning-based pathology detection and localization in 3D chest CT scans. Med Phys 2024. [PMID: 39140793 DOI: 10.1002/mp.17302] [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: 12/11/2023] [Revised: 05/24/2024] [Accepted: 07/01/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Recent advancements in anomaly detection have paved the way for novel radiological reading assistance tools that support the identification of findings, aimed at saving time. The clinical adoption of such applications requires a low rate of false positives while maintaining high sensitivity. PURPOSE In light of recent interest and development in multi pathology identification, we present a novel method, based on a recent contrastive self-supervised approach, for multiple chest-related abnormality identification including low lung density area ("LLDA"), consolidation ("CONS"), nodules ("NOD") and interstitial pattern ("IP"). Our approach alerts radiologists about abnormal regions within a computed tomography (CT) scan by providing 3D localization. METHODS We introduce a new method for the classification and localization of multiple chest pathologies in 3D Chest CT scans. Our goal is to distinguish four common chest-related abnormalities: "LLDA", "CONS", "NOD", "IP" and "NORMAL". This method is based on a 3D patch-based classifier with a Resnet backbone encoder pretrained leveraging recent contrastive self supervised approach and a fine-tuned classification head. We leverage the SimCLR contrastive framework for pretraining on an unannotated dataset of randomly selected patches and we then fine-tune it on a labeled dataset. During inference, this classifier generates probability maps for each abnormality across the CT volume, which are aggregated to produce a multi-label patient-level prediction. We compare different training strategies, including random initialization, ImageNet weight initialization, frozen SimCLR pretrained weights and fine-tuned SimCLR pretrained weights. Each training strategy is evaluated on a validation set for hyperparameter selection and tested on a test set. Additionally, we explore the fine-tuned SimCLR pretrained classifier for 3D pathology localization and conduct qualitative evaluation. RESULTS Validated on 111 chest scans for hyperparameter selection and subsequently tested on 251 chest scans with multi-abnormalities, our method achieves an AUROC of 0.931 (95% confidence interval [CI]: [0.9034, 0.9557], p $ p$ -value < 0.001) and 0.963 (95% CI: [0.952, 0.976], p $ p$ -value < 0.001) in the multi-label and binary (i.e., normal versus abnormal) settings, respectively. Notably, our method surpasses the area under the receiver operating characteristic (AUROC) threshold of 0.9 for two abnormalities: IP (0.974) and LLDA (0.952), while achieving values of 0.853 and 0.791 for NOD and CONS, respectively. Furthermore, our results highlight the superiority of incorporating contrastive pretraining within the patch classifier, outperforming Imagenet pretraining weights and non-pretrained counterparts with uninitialized weights (F1 score = 0.943, 0.792, and 0.677 respectively). Qualitatively, the method achieved a satisfactory 88.8% completeness rate in localization and maintained an 88.3% accuracy rate against false positives. CONCLUSIONS The proposed method integrates self-supervised learning algorithms for pretraining, utilizes a patch-based approach for 3D pathology localization and develops an aggregation method for multi-label prediction at patient-level. It shows promise in efficiently detecting and localizing multiple anomalies within a single scan.
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Affiliation(s)
- Aissam Djahnine
- CREATIS UMR5220, INSERM U1044, Claude Bernard University Lyon 1, INSA, Lyon, France
- Philips Health Technology innovation, Paris, France
| | | | | | - Salim Aymeric Si-Mohamed
- CREATIS UMR5220, INSERM U1044, Claude Bernard University Lyon 1, INSA, Lyon, France
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | | | - Vincent Cottin
- National Reference Center for Rare Pulmonary Diseases, Louis Pradel Hospital, Lyon, France
- Claude Bernard University Lyon 1, Lyon, France
| | - Philippe Douek
- CREATIS UMR5220, INSERM U1044, Claude Bernard University Lyon 1, INSA, Lyon, France
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | | | - Loic Boussel
- CREATIS UMR5220, INSERM U1044, Claude Bernard University Lyon 1, INSA, Lyon, France
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
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2
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Kim H, Lee S, Shim WJ, Choi MS, Cho S. Homogenization of multi-institutional chest x-ray images in various data transformation schemes. J Med Imaging (Bellingham) 2023; 10:061103. [PMID: 37125408 PMCID: PMC10132786 DOI: 10.1117/1.jmi.10.6.061103] [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: 07/07/2022] [Accepted: 04/03/2023] [Indexed: 05/02/2023] Open
Abstract
Purpose Although there are several options for improving the generalizability of learned models, a data instance-based approach is desirable when stable data acquisition conditions cannot be guaranteed. Despite the wide use of data transformation methods to reduce data discrepancies between different data domains, detailed analysis for explaining the performance of data transformation methods is lacking. Approach This study compares several data transformation methods in the tuberculosis detection task with multi-institutional chest x-ray (CXR) data. Five different data transformations, including normalization, standardization with and without lung masking, and multi-frequency-based (MFB) standardization with and without lung masking were implemented. A tuberculosis detection network was trained using a reference dataset, and the data from six other sites were used for the network performance comparison. To analyze data harmonization performance, we extracted radiomic features and calculated the Mahalanobis distance. We visualized the features with a dimensionality reduction technique. Through similar methods, deep features of the trained networks were also analyzed to examine the models' responses to the data from various sites. Results From various numerical assessments, the MFB standardization with lung masking provided the highest network performance for the non-reference datasets. From the radiomic and deep feature analyses, the features of the multi-site CXRs after MFB with lung masking were found to be well homogenized to the reference data, whereas the others showed limited performance. Conclusions Conventional normalization and standardization showed suboptimal performance in minimizing feature differences among various sites. Our study emphasizes the strengths of MFB standardization with lung masking in terms of network performance and feature homogenization.
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Affiliation(s)
- Hyeongseok Kim
- KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Seoyoung Lee
- Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering, Daejeon, Republic of Korea
| | - Woo Jung Shim
- AI Research Center, Radisen Co., Ltd., Seoul, Republic of Korea
| | - Min-Seong Choi
- AI Research Center, Radisen Co., Ltd., Seoul, Republic of Korea
| | - Seungryong Cho
- KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- Korea Advanced Institute of Science and Technology, Department of Nuclear and Quantum Engineering, Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- KAIST Institute for IT Convergence, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Celik G. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Comput Biol Med 2023; 163:107153. [PMID: 37321101 PMCID: PMC10249348 DOI: 10.1016/j.compbiomed.2023.107153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 06/17/2023]
Abstract
This study proposes a new deep learning-based method that demonstrates high performance in detecting Covid-19 disease from cough, breath, and voice signals. This impressive method, named CovidCoughNet, consists of a deep feature extraction network (InceptionFireNet) and a prediction network (DeepConvNet). The InceptionFireNet architecture, based on Inception and Fire modules, was designed to extract important feature maps. The DeepConvNet architecture, which is made up of convolutional neural network blocks, was developed to predict the feature vectors obtained from the InceptionFireNet architecture. The COUGHVID dataset containing cough data and the Coswara dataset containing cough, breath, and voice signals were used as the data sets. The pitch-shifting technique was used to data augmentation the signal data, which significantly contributed to improving performance. Additionally, Chroma features (CF), Root mean square energy (RMSE), Spectral centroid (SC), Spectral bandwidth (SB), Spectral rolloff (SR), Zero crossing rate (ZCR), and Mel frequency cepstral coefficients (MFCC) feature extraction techniques were used to extract important features from voice signals. Experimental studies have shown that using the pitch-shifting technique improved performance by around 3% compared to raw signals. When the proposed model was used with the COUGHVID dataset (Healthy, Covid-19, and Symptomatic), a high performance of 99.19% accuracy, 0.99 precision, 0.98 recall, 0.98 F1-Score, 97.77% specificity, and 98.44% AUC was achieved. Similarly, when the voice data in the Coswara dataset was used, higher performance was achieved compared to the cough and breath studies, with 99.63% accuracy, 100% precision, 0.99 recall, 0.99 F1-Score, 99.24% specificity, and 99.24% AUC. Moreover, when compared with current studies in the literature, the proposed model was observed to exhibit highly successful performance. The codes and details of the experimental studies can be accessed from the relevant Github page: (https://github.com/GaffariCelik/CovidCoughNet).
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Affiliation(s)
- Gaffari Celik
- Agri Ibrahim Cecen University, Department of Computer Technology, Agri, Turkey.
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4
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [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] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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Bhattacharjee V, Priya A, Kumari N, Anwar S. DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images. WIRELESS PERSONAL COMMUNICATIONS 2023; 130:1399-1416. [PMID: 37168437 PMCID: PMC10088652 DOI: 10.1007/s11277-023-10336-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 05/13/2023]
Abstract
COVID-19 is an epidemic disease that has threatened all the people at worldwide scale and eventually became a pandemic It is a crucial task to differentiate COVID-19-affected patients from healthy patient populations. The need for technology enabled solutions is pertinent and this paper proposes a deep learning model for detection of COVID-19 using Chest X-Ray (CXR) images. In this research work, we provide insights on how to build robust deep learning based models for COVID-19 CXR image classification from Normal and Pneumonia affected CXR images. We contribute a methodical escort on preparation of data to produce a robust deep learning model. The paper prepared datasets by refactoring, using images from several datasets for ameliorate training of deep model. These recently published datasets enable us to build our own model and compare by using pre-trained models. The proposed experiments show the ability to work effectively to classify COVID-19 patients utilizing CXR. The empirical work, which uses a 3 convolutional layer based Deep Neural Network called "DeepCOVNet" to classify CXR images into 3 classes: COVID-19, Normal and Pneumonia cases, yielded an accuracy of 96.77% and a F1-score of 0.96 on two different combination of datasets.
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Affiliation(s)
| | - Ankita Priya
- Birla Institute of Technology Mesra, Ranchi, 835215 India
| | - Nandini Kumari
- Birla Institute of Technology Mesra, Ranchi, 835215 India
- Department of Data Science & Computer Application, Manipal Institute of Technology, Manipal, Manipal Academy of Higher Education, Manipal, 576104 Karnataka India
| | - Shamama Anwar
- Birla Institute of Technology Mesra, Ranchi, 835215 India
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Deep Survival Analysis With Clinical Variables for COVID-19. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:223-231. [PMID: 36950264 PMCID: PMC10027076 DOI: 10.1109/jtehm.2023.3256966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 01/08/2023] [Accepted: 03/08/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. METHODS AND PROCEDURES We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups. RESULTS Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19. CONCLUSION Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner. CLINICAL IMPACT The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.
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Kathamuthu ND, Subramaniam S, Le QH, Muthusamy S, Panchal H, Sundararajan SCM, Alrubaie AJ, Zahra MMA. A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. ADVANCES IN ENGINEERING SOFTWARE (BARKING, LONDON, ENGLAND : 1992) 2023; 175:103317. [PMID: 36311489 PMCID: PMC9595382 DOI: 10.1016/j.advengsoft.2022.103317] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/09/2022] [Accepted: 10/19/2022] [Indexed: 05/26/2023]
Abstract
The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.
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Affiliation(s)
- Nirmala Devi Kathamuthu
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Shanthi Subramaniam
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Quynh Hoang Le
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
| | - Suresh Muthusamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Hitesh Panchal
- Department of Mechanical Engineering, Government Engineering College, Patan, Gujarat, India
| | | | - Ali Jawad Alrubaie
- Department of Medical Instrumentation Techniques Engineering, Al- Mustaqbal University College, 51001, Hilla, Iraq
| | - Musaddak Maher Abdul Zahra
- Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
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8
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Irkham I, Ibrahim AU, Nwekwo CW, Al-Turjman F, Hartati YW. Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review. SENSORS (BASEL, SWITZERLAND) 2022; 23:426. [PMID: 36617023 PMCID: PMC9824404 DOI: 10.3390/s23010426] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/14/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.
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Affiliation(s)
- Irkham Irkham
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
| | | | - Chidi Wilson Nwekwo
- Department of Biomedical Engineering, Near East University, Mersin 99138, Turkey
| | - Fadi Al-Turjman
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 99138, Turkey
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 99138, Turkey
| | - Yeni Wahyuni Hartati
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung 40173, Indonesia
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9
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Merchant SA, Nadkarni P, Shaikh MJS. Augmentation of literature review of COVID-19 radiology. World J Radiol 2022; 14:342-351. [PMID: 36186515 PMCID: PMC9521431 DOI: 10.4329/wjr.v14.i9.342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/26/2022] [Accepted: 08/21/2022] [Indexed: 02/08/2023] Open
Abstract
We suggest an augmentation of the excellent comprehensive review article titled "Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the coronavirus disease 2019 (COVID-19) pandemic" under the following categories: (1) "Inclusion of additional radiological features, related to pulmonary infarcts and to COVID-19 pneumonia"; (2) "Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis"; (3) "Imaging findings related to fluorodeoxyglucose positron emission tomography, optical, thermal and other imaging modalities/devices, including 'intelligent edge' and other remote monitoring devices"; (4) "Artificial intelligence in COVID-19 imaging"; (5) "Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed"; and (6) "A minor correction to a passage on pulmonary destruction".
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Affiliation(s)
| | - Prakash Nadkarni
- College of Nursing, University of Iowa, Iowa City, IA 52242, United States
| | - Mohd Javed Saifullah Shaikh
- Department of Radiology, North Bengal Neuro Centre - Jupiter MRI & Diagnostic Centre, Siliguri 734003, West Bengal, India
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10
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Ali Ahmed SA, Yavuz MC, Şen MU, Gülşen F, Tutar O, Korkmazer B, Samancı C, Şirolu S, Hamid R, Eryürekli AE, Mammadov T, Yanikoglu B. Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images. Neurocomputing 2022; 488:457-469. [PMID: 35345875 PMCID: PMC8942080 DOI: 10.1016/j.neucom.2022.02.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/06/2021] [Accepted: 02/03/2022] [Indexed: 12/31/2022]
Abstract
Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the RT-PCR test. We compare slice-based (2D) and volume-based (3D) approaches to this problem and propose a deep learning ensemble, called IST-CovNet, combining the best 2D and 3D systems with novel preprocessing and attention modules and the use of a bidirectional Long Short-Term Memory model for combining slice-level decisions. The proposed ensemble obtains 90.80% accuracy and 0.95 AUC score overall on the newly collected IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMedData dataset that consists of COVID-19 scans and normal controls only. The system also obtains state-of-art results (90.16% accuracy and 0.94 AUC) on the COVID-CT-MD dataset which is only used for testing. The system is deployed at Istanbul University Cerrahpaşa School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.
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Affiliation(s)
- Sara Atito Ali Ahmed
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, U.K.7XH, UK
| | - Mehmet Can Yavuz
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Mehmet Umut Şen
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Fatih Gülşen
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Onur Tutar
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Bora Korkmazer
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Cesur Samancı
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Sabri Şirolu
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Rauf Hamid
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Ali Ergun Eryürekli
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Toghrul Mammadov
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Berrin Yanikoglu
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
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Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PSC, Saba L, Mehmedović A, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Fouda MM, Naidu S, Viskovic K, Kalra MK. COVLIAS 1.0 Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans. Diagnostics (Basel) 2022; 12:1283. [PMID: 35626438 PMCID: PMC9141749 DOI: 10.3390/diagnostics12051283] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 02/01/2023] Open
Abstract
Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Armin Mehmedović
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95661, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Klaudija Viskovic
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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12
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Chaddad A, Hassan L, Desrosiers C. Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-Ray Images. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3-11. [PMID: 34669582 DOI: 10.1109/tnnls.2021.3119071] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This article proposes to encode the distribution of features learned from a convolutional neural network (CNN) using a Gaussian mixture model (GMM). These parametric features, called GMM-CNN, are derived from chest computed tomography (CT) and X-ray scans of patients with coronavirus disease 2019 (COVID-19). We use the proposed GMM-CNN features as input to a robust classifier based on random forests (RFs) to differentiate between COVID-19 and other pneumonia cases. Our experiments assess the advantage of GMM-CNN features compared with standard CNN classification on test images. Using an RF classifier (80% samples for training; 20% samples for testing), GMM-CNN features encoded with two mixture components provided a significantly better performance than standard CNN classification ( ). Specifically, our method achieved an accuracy in the range of 96.00%-96.70% and an area under the receiver operator characteristic (ROC) curve in the range of 99.29%-99.45%, with the best performance obtained by combining GMM-CNN features from both CT and X-ray images. Our results suggest that the proposed GMM-CNN features could improve the prediction of COVID-19 in chest CT and X-ray scans.
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13
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Suri JS, Agarwal S, Carriero A, Paschè A, Danna PSC, Columbu M, Saba L, Viskovic K, Mehmedović A, Agarwal S, Gupta L, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Gupta A, Naidu S, Paraskevas KI, Kalra MK. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts. Diagnostics (Basel) 2021; 11:diagnostics11122367. [PMID: 34943603 PMCID: PMC8699928 DOI: 10.3390/diagnostics11122367] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/29/2021] [Accepted: 12/13/2021] [Indexed: 02/07/2023] Open
Abstract
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia; (K.V.); (A.M.)
| | - Armin Mehmedović
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia; (K.V.); (A.M.)
| | - Samriddhi Agarwal
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India
| | - Lakshya Gupta
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
| | - Gavino Faa
- Department of Pathology, AOU of Cagliari, 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | | | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
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14
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Suri JS, Agarwal S, Elavarthi P, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Ferenc N, Ruzsa Z, Gupta A, Naidu S, Kalra MK. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography. Diagnostics (Basel) 2021; 11:2025. [PMID: 34829372 PMCID: PMC8625039 DOI: 10.3390/diagnostics11112025] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 02/05/2023] Open
Abstract
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Pranav Elavarthi
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Rajesh Pathak
- Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492001, India;
| | | | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Suneet K. Gupta
- Department of Computer Science, Bennett University, Noida 201310, India;
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 10558 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National & Kapodistrian University of Athens, 10679 Athens, Greece;
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, 54636 Thessaloniki, Greece;
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PT, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2368, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Nagy Ferenc
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Zoltan Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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15
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Abdelwhab Ouahab. Multimodal Convolutional Neural Networks for Detection of Covid-19 Using Chest X-Ray and CT Images. OPTICAL MEMORY AND NEURAL NETWORKS 2021. [PMCID: PMC8715284 DOI: 10.3103/s1060992x21040044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The Covid-19 was first appeared in 2019 in Wuhan, China. It widely and rapidly expanded all over the world. Since then, it has had a strong effect on people’s daily lives, the world economy and the public health. The fast prediction of Covid-19 can assist the medicine to choose the right treatment. In this paper, we propose a classification of Covid-19 using Models based on a Convolutional Neural Network (CNN). We propose two models to detect Covid-19. The first one uses CNN with CT or X-ray images separately. The second uses CNN with both CT and X-ray images at the same time. The used datasets contain X-ray and CT images divided into three classes which are Covid-19, Normal and Pneumonia. Each type image class has 1045 images for training and 300 for testing. All these data sets are available in Kaggle repository. In order to evaluate the proposed models, we calculate the confusion matrix, the accuracy, precision, recall and F1 score. The model that uses CNN with both X-ray and CT images of 0.99 achieves the best accuracy. We deduced that using CT images is more efficient than using X-ray images to predict Covid-19. The combination of the CT and X-ray images to detect Covid-19 is more efficient than using only CT or X-ray images. The proposed models could effectively assist the radiologists in predicting Covid-19.
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16
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Toğaçar M, Muzoğlu N, Ergen B, Yarman BSB, Halefoğlu AM. Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs. Biomed Signal Process Control 2021; 71:103128. [PMID: 34490055 PMCID: PMC8410514 DOI: 10.1016/j.bspc.2021.103128] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/23/2021] [Accepted: 08/30/2021] [Indexed: 12/17/2022]
Abstract
Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach.
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Affiliation(s)
- Mesut Toğaçar
- Department of Computer Technologies, Technical Sciences Vocational School, Fırat University, Elazig, Turkey
| | - Nedim Muzoğlu
- Department of Biomedical Sciences, Faculty of Engineering, Istanbul University, Istanbul, Turkey
| | - Burhan Ergen
- Department of Computer Engineering, Faculty of Engineering, Fırat University, Elazig, Turkey
| | - Bekir Sıddık Binboğa Yarman
- Department of Electric-Electronic Engineering, Faculty of Engineering, Istanbul University, Istanbul, Turkey
| | - Ahmet Mesrur Halefoğlu
- Department of Radiology, Şişli Hamidiye Etfal Training and Research Hospital, Health Sciences University, Istanbul, Turkey
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17
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Bouchareb Y, Moradi Khaniabadi P, Al Kindi F, Al Dhuhli H, Shiri I, Zaidi H, Rahmim A. Artificial intelligence-driven assessment of radiological images for COVID-19. Comput Biol Med 2021; 136:104665. [PMID: 34343890 PMCID: PMC8291996 DOI: 10.1016/j.compbiomed.2021.104665] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/11/2021] [Accepted: 07/17/2021] [Indexed: 12/24/2022]
Abstract
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.
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Affiliation(s)
- Yassine Bouchareb
- Department of Radiology and Molecular Imaging, College of Medicine and Health Science, Sultan Qaboos University, PO. Box 35, Al Khod, Muscat, 123, Oman.
| | - Pegah Moradi Khaniabadi
- Department of Radiology and Molecular Imaging, College of Medicine and Health Science, Sultan Qaboos University, PO. Box 35, Al Khod, Muscat, 123, Oman.
| | | | - Humoud Al Dhuhli
- Department of Radiology and Molecular Imaging, College of Medicine and Health Science, Sultan Qaboos University, PO. Box 35, Al Khod, Muscat, 123, Oman
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
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18
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Suri JS, Agarwal S, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Frence N, Ruzsa Z, Gupta A, Naidu S, Kalra M. COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models. Diagnostics (Basel) 2021; 11:1405. [PMID: 34441340 PMCID: PMC8392426 DOI: 10.3390/diagnostics11081405] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. METHODOLOGY The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. RESULTS Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. CONCLUSIONS The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Rajesh Pathak
- Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492015, India;
| | | | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Suneet K. Gupta
- Department of Computer Science, Bennett University, Noida 201310, India;
| | - Gavino Faa
- Department of Pathology—AOU of Cagliari, 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 208011, India;
| | - Klaudija Viskovic
- Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 176 74 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence City, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital Providence, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence City, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 157 72 Athens, Greece;
| | - George Tsoulfas
- Department of Transplantation Surgery, Aristoteleion University of Thessaloniki, 541 24 Thessaloniki, Greece;
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5G 1N8, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- Athero Point LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Nagy Frence
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary; (N.F.); (Z.R.)
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary; (N.F.); (Z.R.)
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55455, USA;
| | - Mannudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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