151
|
Mei X, Liu Z, Robson PM, Marinelli B, Huang M, Doshi A, Jacobi A, Cao C, Link KE, Yang T, Wang Y, Greenspan H, Deyer T, Fayad ZA, Yang Y. RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning. RADIOLOGY: ARTIFICIAL INTELLIGENCE 2022; 4:e210315. [PMID: 36204533 PMCID: PMC9530758 DOI: 10.1148/ryai.210315] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/31/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022]
Abstract
Purpose To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical applications when using transfer learning. Materials and Methods This retrospective study included patients who underwent a radiologic study between 2005 and 2020 at an outpatient imaging facility. Key images and associated labels from the studies were retrospectively extracted from the original study interpretation. These images were used for RadImageNet model training with random weight initiation. The RadImageNet models were compared with ImageNet models using the area under the receiver operating characteristic curve (AUC) for eight classification tasks and using Dice scores for two segmentation problems. Results The RadImageNet database consists of 1.35 million annotated medical images in 131 872 patients who underwent CT, MRI, and US for musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, abdominal, and pulmonary pathologic conditions. For transfer learning tasks on small datasets—thyroid nodules (US), breast masses (US), anterior cruciate ligament injuries (MRI), and meniscal tears (MRI)—the RadImageNet models demonstrated a significant advantage (P < .001) to ImageNet models (9.4%, 4.0%, 4.8%, and 4.5% AUC improvements, respectively). For larger datasets—pneumonia (chest radiography), COVID-19 (CT), SARS-CoV-2 (CT), and intracranial hemorrhage (CT)—the RadImageNet models also illustrated improved AUC (P < .001) by 1.9%, 6.1%, 1.7%, and 0.9%, respectively. Additionally, lesion localizations of the RadImageNet models were improved by 64.6% and 16.4% on thyroid and breast US datasets, respectively. Conclusion RadImageNet pretrained models demonstrated better interpretability compared with ImageNet models, especially for smaller radiologic datasets. Keywords: CT, MR Imaging, US, Head/Neck, Thorax, Brain/Brain Stem, Evidence-based Medicine, Computer Applications–General (Informatics) Supplemental material is available for this article. Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.
Collapse
|
152
|
Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features. Tomography 2022; 8:1791-1803. [PMID: 35894016 PMCID: PMC9326627 DOI: 10.3390/tomography8040151] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 11/17/2022] Open
Abstract
The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical features was trained to predict mortality. The Local Interpretable Model-Agnostic Explanations (LIME) method was used to explain model predictions. Non-imaging data alone predicted mortality with an ROC-AUC of 0.87 ± 0.03 (mean ± SD), while the addition of imaging data improved prediction slightly (ROC-AUC: 0.91 ± 0.02). The application of LIME to the combined imaging and clinical model found HbA1c values to contribute the most to model prediction (17.1 ± 1.7%), while imaging contributed 8.8 ± 2.8%. Age, gender, and BMI contributed 8.7%, 8.2%, and 7.1%, respectively. Our findings demonstrate a viable explainable AI approach to quantify the contributions of imaging and clinical data to COVID mortality predictions.
Collapse
|
153
|
Alshayeji MH, ChandraBhasi Sindhu S, Abed S. CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images. BMC Bioinformatics 2022; 23:264. [PMID: 35794537 PMCID: PMC9261058 DOI: 10.1186/s12859-022-04818-4] [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: 03/10/2022] [Accepted: 06/30/2022] [Indexed: 11/19/2022] Open
Abstract
Background Here propose a computer-aided diagnosis (CAD) system to differentiate COVID-19 (the coronavirus disease of 2019) patients from normal cases, as well as to perform infection region segmentation along with infection severity estimation using computed tomography (CT) images. The developed system facilitates timely administration of appropriate treatment by identifying the disease stage without reliance on medical professionals. So far, this developed model gives the most accurate, fully automatic COVID-19 real-time CAD framework. Results The CT image dataset of COVID-19 and non-COVID-19 individuals were subjected to conventional ML stages to perform binary classification. In the feature extraction stage, SIFT, SURF, ORB image descriptors and bag of features technique were implemented for the appropriate differentiation of chest CT regions affected with COVID-19 from normal cases. This is the first work introducing this concept for COVID-19 diagnosis application. The preferred diverse database and selected features that are invariant to scale, rotation, distortion, noise etc. make this framework real-time applicable. Also, this fully automatic approach which is faster compared to existing models helps to incorporate it into CAD systems. The severity score was measured based on the infected regions along the lung field. Infected regions were segmented through a three-class semantic segmentation of the lung CT image. Using severity score, the disease stages were classified as mild if the lesion area covers less than 25% of the lung area; moderate if 25–50% and severe if greater than 50%. Our proposed model resulted in classification accuracy of 99.7% with a PNN classifier, along with area under the curve (AUC) of 0.9988, 99.6% sensitivity, 99.9% specificity and a misclassification rate of 0.0027. The developed infected region segmentation model gave 99.47% global accuracy, 94.04% mean accuracy, 0.8968 mean IoU (intersection over union), 0.9899 weighted IoU, and a mean Boundary F1 (BF) contour matching score of 0.9453, using Deepabv3+ with its weights initialized using ResNet-50. Conclusions The developed CAD system model is able to perform fully automatic and accurate diagnosis of COVID-19 along with infected region extraction and disease stage identification. The ORB image descriptor with bag of features technique and PNN classifier achieved the superior classification performance.
Collapse
Affiliation(s)
- Mohammad H Alshayeji
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, 13060, Safat, Kuwait City, Kuwait.
| | | | - Sa'ed Abed
- Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, 13060, Safat, Kuwait City, Kuwait
| |
Collapse
|
154
|
Ahmed MM, Sayed AM, El Abd D, El Sayed IT, Elkholy YS, Fares AH, Fares S. Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals. J Int Med Res 2022; 50:3000605221109392. [PMID: 35861236 PMCID: PMC9310293 DOI: 10.1177/03000605221109392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVES Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to correlate clinical and laboratory data of patients with COVID-19 and their polymerase chain reaction (PCR) results and to assess the accuracy of a deep learning model in diagnosing COVID-19. METHODS This was a retrospective study using an anonymized dataset of patients with suspected COVID-19. Only patients with a complete dataset were included (n = 440). A deep analytics framework and dual-modal approach for PCR-based classification was used, integrating symptoms and laboratory-based modalities. RESULTS Participants with loss of smell or taste were two times more likely to have positive PCR results (odds ratio [OR] 1.86). Participants with neutropenia, high serum ferritin, or monocytosis were three, four, and five times more likely to have positive PCR results (OR 2.69, 4.18, 5.42, respectively). The rate of accuracy achieved using the deep learning framework was 78%, with sensitivity of 83.9% and specificity of 71.4%. CONCLUSION Loss of smell or taste, neutropenia, monocytosis, and high serum ferritin should be routinely assessed with suspected COVID-19 infection. The use of deep learning for diagnosis is a promising tool that can be implemented in the primary care setting.
Collapse
Affiliation(s)
- Marwa M Ahmed
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Amal M Sayed
- Department of Clinical & Chemical Pathology, Kasralainy, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Dina El Abd
- Department of Clinical & Chemical Pathology, Kasralainy, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Inas T El Sayed
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Yasmine S Elkholy
- Department of Medical Microbiology & Immunology, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ahmed H Fares
- Computer Science and Engineering Department, Faculty of Engineering, Benha University, Cairo, Egypt
| | - Samar Fares
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| |
Collapse
|
155
|
Pezoulas VC, Liontos A, Mylona E, Papaloukas C, Milionis O, Biros D, Kyriakopoulos C, Kostikas K, Milionis H, Fotiadis DI. Predicting the need for mechanical ventilation and mortality in hospitalized COVID-19 patients who received heparin. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1020-1023. [PMID: 36086001 DOI: 10.1109/embc48229.2022.9871261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Although several studies have utilized AI (artificial intelligence)-based solutions to enhance the decision making for mechanical ventilation, as well as, for mortality in COVID-19, the extraction of explainable predictors regarding heparin's effect in intensive care and mortality has been left unresolved. In the present study, we developed an explainable AI (XAI) workflow to shed light into predictors for admission in the intensive care unit (ICU), as well as, for mortality across those hospitalized COVID-19 patients who received heparin. AI empowered classifiers, such as, the hybrid Extreme gradient boosting (HXGBoost) with customized loss functions were trained on time-series curated clinical data to develop robust AI models. Shapley additive explanation analysis (SHAP) was conducted to determine the positive or negative impact of the predictors in the model's output. The HXGBoost predicted the risk for intensive care and mortality with 0.84 and 0.85 accuracy, respectively. SHAP analysis indicated that the low percentage of lymphocytes at day 7 along with increased FiO2 at days 1 and 5, low SatO2 at days 3 and 7 increase the probability for mortality and highlight the positive effect of heparin administration at the early days of hospitalization for reducing mortality.
Collapse
|
156
|
Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network. SENSORS 2022; 22:s22134820. [PMID: 35808317 PMCID: PMC9269123 DOI: 10.3390/s22134820] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/16/2022] [Accepted: 06/23/2022] [Indexed: 01/08/2023]
Abstract
Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.
Collapse
|
157
|
Gong H, Wang M, Zhang H, Elahe MF, Jin M. An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms. Front Public Health 2022; 10:874455. [PMID: 35801239 PMCID: PMC9253566 DOI: 10.3389/fpubh.2022.874455] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
Abstract
Background Artificial intelligence-based disease prediction models have a greater potential to screen COVID-19 patients than conventional methods. However, their application has been restricted because of their underlying black-box nature. Objective To addressed this issue, an explainable artificial intelligence (XAI) approach was developed to screen patients for COVID-19. Methods A retrospective study consisting of 1,737 participants (759 COVID-19 patients and 978 controls) admitted to San Raphael Hospital (OSR) from February to May 2020 was used to construct a diagnosis model. Finally, 32 key blood test indices from 1,374 participants were used for screening patients for COVID-19. Four ensemble learning algorithms were used: random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). Feature importance from the perspective of the clinical domain and visualized interpretations were illustrated by using local interpretable model-agnostic explanations (LIME) plots. Results The GBDT model [area under the curve (AUC): 86.4%; 95% confidence interval (CI) 0.821–0.907] outperformed the RF model (AUC: 85.7%; 95% CI 0.813–0.902), AdaBoost model (AUC: 85.4%; 95% CI 0.810–0.899), and XGBoost model (AUC: 84.9%; 95% CI 0.803–0.894) in distinguishing patients with COVID-19 from those without. The cumulative feature importance of lactate dehydrogenase, white blood cells, and eosinophil counts was 0.145, 0.130, and 0.128, respectively. Conclusions Ensemble machining learning (ML) approaches, mainly GBDT and LIME plots, are efficient for screening patients with COVID-19 and might serve as a potential tool in the auxiliary diagnosis of COVID-19. Patients with higher WBC count, higher LDH level, or higher EOT count, were more likely to have COVID-19.
Collapse
Affiliation(s)
- Houwu Gong
- Department of Software Engineering, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
- Academy of Military Sciences, Beijing, China
| | - Miye Wang
- Engineering Research Center of Medical Information Technology, Ministry of Education, West China Hospital, Chengdu, China
- Information Center, West China Hospital, Chengdu, China
| | - Hanxue Zhang
- Department of Software Engineering, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Md Fazla Elahe
- Department of Software Engineering, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Min Jin
- Department of Software Engineering, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
- *Correspondence: Min Jin
| |
Collapse
|
158
|
Suganyadevi S, Seethalakshmi V. CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network. WIRELESS PERSONAL COMMUNICATIONS 2022; 126:3279-3303. [PMID: 35756172 PMCID: PMC9206838 DOI: 10.1007/s11277-022-09864-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/29/2022] [Indexed: 06/04/2023]
Abstract
The use of computer-assisted analysis to improve image interpretation has been a long-standing challenge in the medical imaging industry. In terms of image comprehension, Continuous advances in AI (Artificial Intelligence), predominantly in DL (Deep Learning) techniques, are supporting in the classification, Detection, and quantification of anomalies in medical images. DL techniques are the most rapidly evolving branch of AI, and it's recently been successfully pragmatic in a variety of fields, including medicine. This paper provides a classification method for COVID 19 infected X-ray images based on new novel deep CNN model. For COVID19 specified pneumonia analysis, two new customized CNN architectures, CVD-HNet1 (COVID-HybridNetwork1) and CVD-HNet2 (COVID-HybridNetwork2), have been designed. The suggested method utilizes operations based on boundaries and regions, as well as convolution processes, in a systematic manner. In comparison to existing CNNs, the suggested classification method achieves excellent Accuracy 98 percent, F Score 0.99 and MCC 0.97. These results indicate impressive classification accuracy on a limited dataset, with more training examples, much better results can be achieved. Overall, our CVD-HNet model could be a useful tool for radiologists in diagnosing and detecting COVID 19 instances early.
Collapse
Affiliation(s)
- S. Suganyadevi
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu 641 407 India
| | - V. Seethalakshmi
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu 641 407 India
| |
Collapse
|
159
|
Becker J, Decker JA, Römmele C, Kahn M, Messmann H, Wehler M, Schwarz F, Kroencke T, Scheurig-Muenkler C. Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs. Diagnostics (Basel) 2022; 12:diagnostics12061465. [PMID: 35741276 PMCID: PMC9221818 DOI: 10.3390/diagnostics12061465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 06/10/2022] [Accepted: 06/12/2022] [Indexed: 11/24/2022] Open
Abstract
Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage.
Collapse
Affiliation(s)
- Judith Becker
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
| | - Josua A. Decker
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
| | - Christoph Römmele
- Department of Gastroenterology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.R.); (M.K.); (H.M.)
| | - Maria Kahn
- Department of Gastroenterology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.R.); (M.K.); (H.M.)
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (C.R.); (M.K.); (H.M.)
| | - Markus Wehler
- Department of Internal Medicine IV, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany;
- Emergency Department, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Florian Schwarz
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
| | - Thomas Kroencke
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
- Correspondence: ; Tel.: +49-821-400-2441
| | - Christian Scheurig-Muenkler
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany; (J.B.); (J.A.D.); (F.S.); (C.S.-M.)
| |
Collapse
|
160
|
Lung’s Segmentation Using Context-Aware Regressive Conditional GAN. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
After declaring COVID-19 pneumonia as a pandemic, researchers promptly advanced to seek solutions for patients fighting this fatal disease. Computed tomography (CT) scans offer valuable insight into how COVID-19 infection affects the lungs. Analysis of CT scans is very significant, especially when physicians are striving for quick solutions. This study successfully segmented lung infection due to COVID-19 and provided a physician with a quantitative analysis of the condition. COVID-19 lesions often occur near and over parenchyma walls, which are denser and exhibit lower contrast than the tissues outside the parenchyma. We applied Adoptive Wallis and Gaussian filter alternatively to regulate the outlining of the lungs and lesions near the parenchyma. We proposed a context-aware conditional generative adversarial network (CGAN) with gradient penalty and spectral normalization for automatic segmentation of lungs and lesion segmentation. The proposed CGAN implements higher-order statistics when compared to traditional deep-learning models. The proposed CGAN produced promising results for lung segmentation. Similarly, CGAN has shown outstanding results for COVID-19 lesions segmentation with an accuracy of 99.91%, DSC of 92.91%, and AJC of 92.91%. Moreover, we achieved an accuracy of 99.87%, DSC of 96.77%, and AJC of 95.59% for lung segmentation. Additionally, the suggested network attained a sensitivity of 100%, 81.02%, 76.45%, and 99.01%, respectively, for critical, severe, moderate, and mild infection severity levels. The proposed model outperformed state-of-the-art techniques for the COVID-19 segmentation and detection cases.
Collapse
|
161
|
Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khateri M, Bijari S, Atashzar MR, Shayesteh SP, Khosravi B, Babaei MR, Jenabi E, Hasanian M, Shahhamzeh A, Foroghi Ghomi SY, Mozafari A, Teimouri A, Movaseghi F, Ahmari A, Goharpey N, Bozorgmehr R, Shirzad-Aski H, Mortazavi R, Karimi J, Mortazavi N, Besharat S, Afsharpad M, Abdollahi H, Geramifar P, Radmard AR, Arabi H, Rezaei-Kalantari K, Oveisi M, Rahmim A, Zaidi H. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Comput Biol Med 2022; 145:105467. [PMID: 35378436 PMCID: PMC8964015 DOI: 10.1016/j.compbiomed.2022.105467] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.
Collapse
Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qum, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Ghasemian
- Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran
| | - Ehsan Sharifipour
- Neuroscience Research Center, Qom University of Medical Sciences, Qum, Iran
| | - Saleh Sandoughdaran
- Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Sohrabi
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Elham Sadati
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Somayeh Livani
- Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pooya Iranpour
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Tehran, Iran
| | - Salar Bijari
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Atashzar
- Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran
| | - Bardia Khosravi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Alireza Shahhamzeh
- Clinical Research Development Center, Qom University of Medical Sciences, Qum, Iran
| | - Seyaed Yaser Foroghi Ghomi
- Clinical Research Development Center, Shahid Beheshti Hospital, Qom University Of Medical Sciences, Qom, Iran
| | - Abolfazl Mozafari
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Arash Teimouri
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Fatemeh Movaseghi
- Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran
| | - Azin Ahmari
- Ayatolah Khansary Hospital, Arak University of Medical Sciences, Arak, Iran
| | - Neda Goharpey
- Department of Radiation Oncology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Rama Bozorgmehr
- Clinical Research Development Unit, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Roozbeh Mortazavi
- Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Jalal Karimi
- Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Nazanin Mortazavi
- Dental Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Sima Besharat
- Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Mandana Afsharpad
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Parham Geramifar
- Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - 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
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, 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.
| |
Collapse
|
162
|
Shim SR, Kim SJ, Hong M, Lee J, Kang MG, Han HW. Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis. Diagnostics (Basel) 2022; 12:1302. [PMID: 35741112 PMCID: PMC9222155 DOI: 10.3390/diagnostics12061302] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/04/2022] [Accepted: 05/20/2022] [Indexed: 12/10/2022] Open
Abstract
(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791-0.914) and 0.879 (95% CrI, 0.784-0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960-0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.
Collapse
Affiliation(s)
- Sung Ryul Shim
- Department of Health and Medical Informatics, Kyungnam University College of Health Sciences, Changwon 51767, Korea;
| | - Seong-Jang Kim
- Department of Nuclear Medicine, Pusan National University Yangsan Hospital, Yangsan 50615, Korea;
- Department of Nuclear Medicine, College of Medicine, Pusan National University, Yangsan 50615, Korea
- BioMedical Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan 50615, Korea
| | - Myunghee Hong
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea;
- Institute for Biomedical Informatics, School of Medicine, CHA University, Seongnam 13488, Korea
| | - Jonghoo Lee
- Department of Internal Medicine, Jeju National University Hospital, Jeju National University School of Medicine, Jeju 63241, Korea;
| | - Min-Gyu Kang
- Department of Internal Medicine, Chungbuk National University College of Medicine, Chungbuk National University Hospital, Cheongju 28644, Korea;
| | - Hyun Wook Han
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam 13488, Korea;
- Institute for Biomedical Informatics, School of Medicine, CHA University, Seongnam 13488, Korea
- Institute of Basic Medical Sciences, School of Medicine, CHA University, Seongnam 13488, Korea
| |
Collapse
|
163
|
Rodellar J, Barrera K, Alférez S, Boldú L, Laguna J, Molina A, Merino A. A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection. Bioengineering (Basel) 2022; 9:bioengineering9050229. [PMID: 35621507 PMCID: PMC9137554 DOI: 10.3390/bioengineering9050229] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 11/16/2022] Open
Abstract
Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.
Collapse
Affiliation(s)
- José Rodellar
- Department of Mathematics, Barcelona Est Engineering School, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain;
- Correspondence:
| | - Kevin Barrera
- Department of Mathematics, Barcelona Est Engineering School, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain;
| | - Santiago Alférez
- School of Engineering, Science and Technology, Universidad del Rosario, Bogotá 111711, Colombia;
| | - Laura Boldú
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
| | - Javier Laguna
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
| | - Angel Molina
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
| | - Anna Merino
- Biomedical Diagnostic Center, Core Laboratory, Department of Biochemistry and Molecular Genetics, Hospital Clinic de Barcelona, 08036 Barcelona, Spain; (L.B.); (J.L.); (A.M.); (A.M.)
| |
Collapse
|
164
|
Chandrasekar KS. Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:5381-5395. [PMID: 35645554 PMCID: PMC9126247 DOI: 10.1007/s11831-022-09768-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.
Collapse
|
165
|
Kumar A, Parihar A, Panda U, Parihar DS. Microfluidics-Based Point-of-Care Testing (POCT) Devices in Dealing with Waves of COVID-19 Pandemic: The Emerging Solution. ACS APPLIED BIO MATERIALS 2022; 5:2046-2068. [PMID: 35473316 PMCID: PMC9063993 DOI: 10.1021/acsabm.1c01320] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 04/11/2022] [Indexed: 02/08/2023]
Abstract
Recent advances in microfluidics-based point-of-care testing (POCT) technology such as paper, array, and beads have shown promising results for diagnosing various infectious diseases. The fast and timely detection of viral infection has proven to be a critical step for deciding the therapeutic outcome in the current COVID-19 pandemic, which in turn not only enhances the patient survival rate but also reduces the disease-associated comorbidities. In the present scenario, rapid, noninvasive detection of the virus using low cost and high throughput microfluidics-based POCT devices embraces the advantages over existing diagnostic technologies, for which a centralized lab facility, expensive instruments, sample pretreatment, and skilled personnel are required. Microfluidic-based multiplexed POCT devices can be a boon for clinical diagnosis in developing countries that lacks a centralized health care system and resources. The microfluidic devices can be used for disease diagnosis and exploited for the development and testing of drug efficacy for disease treatment in model systems. The havoc created by the second wave of COVID-19 led several countries' governments to the back front. The lack of diagnostic kits, medical devices, and human resources created a huge demand for a technology that can be remotely operated with single touch and data that can be analyzed on a phone. Recent advancements in information technology and the use of smartphones led to a paradigm shift in the development of diagnostic devices, which can be explored to deal with the current pandemic situation. This review sheds light on various approaches for the development of cost-effective microfluidics POCT devices. The successfully used microfluidic devices for COVID-19 detection under clinical settings along with their pros and cons have been discussed here. Further, the integration of microfluidic devices with smartphones and wireless network systems using the Internet-of-things will enable readers for manufacturing advanced POCT devices for remote disease management in low resource settings.
Collapse
Affiliation(s)
- Avinash Kumar
- Department of Mechanical Engineering,
Indian Institute of Information Technology Design & Manufacturing
Kancheepuram, Chennai 600127, India
| | - Arpana Parihar
- Industrial Waste Utilization, Nano and Biomaterials,
CSIR-Advanced Materials and Processes Research Institute
(AMPRI), Hoshangabad Road, Bhopal, Madhya Pradesh 462026,
India
| | - Udwesh Panda
- Department of Mechanical Engineering,
Indian Institute of Information Technology Design & Manufacturing
Kancheepuram, Chennai 600127, India
| | | |
Collapse
|
166
|
Ebrahimzadeh S, Islam N, Dawit H, Salameh JP, Kazi S, Fabiano N, Treanor L, Absi M, Ahmad F, Rooprai P, Al Khalil A, Harper K, Kamra N, Leeflang MM, Hooft L, van der Pol CB, Prager R, Hare SS, Dennie C, Spijker R, Deeks JJ, Dinnes J, Jenniskens K, Korevaar DA, Cohen JF, Van den Bruel A, Takwoingi Y, van de Wijgert J, Wang J, Pena E, Sabongui S, McInnes MD. Thoracic imaging tests for the diagnosis of COVID-19. Cochrane Database Syst Rev 2022; 5:CD013639. [PMID: 35575286 PMCID: PMC9109458 DOI: 10.1002/14651858.cd013639.pub5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID-19 pneumonia. This new edition is an update of the review. OBJECTIVES Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID-19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT-PCR) negative result and a positive RT-PCR result on follow-up; and evaluate the accuracy of thoracic imaging for screening COVID-19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy. SEARCH METHODS We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 17 February 2021. We did not apply any language restrictions. SELECTION CRITERIA We included diagnostic accuracy studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19. Studies had to assess chest CT, chest X-ray, or ultrasound of the lungs for the diagnosis of COVID-19, use a reference standard that included RT-PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results. DATA COLLECTION AND ANALYSIS The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS-2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. MAIN RESULTS We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID-19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT-PCR negative results and positive RT-PCR results on follow-up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or unclear in seven (7%) studies. Imaging in people with suspected COVID-19 We included 94 studies. Eighty-seven studies evaluated one imaging modality, and seven studies evaluated two imaging modalities. All studies used RT-PCR alone or in combination with other criteria (for example, clinical signs and symptoms, positive contacts) as the reference standard for the diagnosis of COVID-19. For chest CT (69 studies, 28285 participants, 14,342 (51%) cases), sensitivities ranged from 45% to 100%, and specificities from 10% to 99%. The pooled sensitivity of chest CT was 86.9% (95% confidence interval (CI) 83.6 to 89.6), and pooled specificity was 78.3% (95% CI 73.7 to 82.3). Definition for index test positivity was a source of heterogeneity for sensitivity, but not specificity. Reference standard was not a source of heterogeneity. For chest X-ray (17 studies, 8529 participants, 5303 (62%) cases), the sensitivity ranged from 44% to 94% and specificity from 24 to 93%. The pooled sensitivity of chest X-ray was 73.1% (95% CI 64. to -80.5), and pooled specificity was 73.3% (95% CI 61.9 to 82.2). Definition for index test positivity was not found to be a source of heterogeneity. Definition for index test positivity and reference standard were not found to be sources of heterogeneity. For ultrasound of the lungs (15 studies, 2410 participants, 1158 (48%) cases), the sensitivity ranged from 73% to 94% and the specificity ranged from 21% to 98%. The pooled sensitivity of ultrasound was 88.9% (95% CI 84.9 to 92.0), and the pooled specificity was 72.2% (95% CI 58.8 to 82.5). Definition for index test positivity and reference standard were not found to be sources of heterogeneity. Indirect comparisons of modalities evaluated across all 94 studies indicated that chest CT and ultrasound gave higher sensitivity estimates than X-ray (P = 0.0003 and P = 0.001, respectively). Chest CT and ultrasound gave similar sensitivities (P=0.42). All modalities had similar specificities (CT versus X-ray P = 0.36; CT versus ultrasound P = 0.32; X-ray versus ultrasound P = 0.89). Imaging in PCR-negative people who subsequently became positive For rate of positive imaging in individuals with initial RT-PCR negative results, we included 8 studies (7 CT, 1 ultrasound) with a total of 198 participants suspected of having COVID-19, all of whom had a final diagnosis of COVID-19. Most studies (7/8) evaluated CT. Of 177 participants with initially negative RT-PCR who had positive RT-PCR results on follow-up testing, 75.8% (95% CI 45.3 to 92.2) had positive CT findings. Imaging in asymptomatic PCR-positive people For imaging asymptomatic individuals, we included 10 studies (7 CT, 1 X-ray, 2 ultrasound) with a total of 3548 asymptomatic participants, of whom 364 (10%) had a final diagnosis of COVID-19. For chest CT (7 studies, 3134 participants, 315 (10%) cases), the pooled sensitivity was 55.7% (95% CI 35.4 to 74.3) and the pooled specificity was 91.1% (95% CI 82.6 to 95.7). AUTHORS' CONCLUSIONS Chest CT and ultrasound of the lungs are sensitive and moderately specific in diagnosing COVID-19. Chest X-ray is moderately sensitive and moderately specific in diagnosing COVID-19. Thus, chest CT and ultrasound may have more utility for ruling out COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. The uncertainty resulting from high or unclear risk of bias and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.
Collapse
Affiliation(s)
- Sanam Ebrahimzadeh
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Nayaar Islam
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Haben Dawit
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | | | - Sakib Kazi
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | | | - Lee Treanor
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Marissa Absi
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Faraz Ahmad
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Paul Rooprai
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Ahmed Al Khalil
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Kelly Harper
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Neil Kamra
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Mariska Mg Leeflang
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands
| | | | - Ross Prager
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Samanjit S Hare
- Department of Radiology, Royal Free London NHS Trust, London , UK
| | - Carole Dennie
- Department of Radiology, University of Ottawa, Ottawa, Canada
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
| | - René Spijker
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
| | - Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Jacqueline Dinnes
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Kevin Jenniskens
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Daniël A Korevaar
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Jérémie F Cohen
- Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and Statistics (CRESS), UMR1153, Université de Paris, Paris, France
| | | | - Yemisi Takwoingi
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Janneke van de Wijgert
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Institute of Infection, Veterinary, and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Junfeng Wang
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Elena Pena
- Department of Radiology, University of Ottawa, Ottawa, Canada
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
| | | | - Matthew Df McInnes
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Radiology, University of Ottawa, Ottawa, Canada
| |
Collapse
|
167
|
Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN COMPUTER SCIENCE 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
Collapse
Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M’Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d’Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia
- Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E. Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
168
|
Abstract
Healthcare is one of the crucial aspects of the Internet of things. Connected machine learning-based systems provide faster healthcare services. Doctors and radiologists can also use these systems for collaboration to provide better help to patients. The recently emerged Coronavirus (COVID-19) is known to have strong infectious ability. Reverse transcription-polymerase chain reaction (RT-PCR) is recognised as being one of the primary diagnostic tools. However, RT-PCR tests might not be accurate. In contrast, doctors can employ artificial intelligence techniques on X-ray and CT scans for analysis. Artificial intelligent methods need a large number of images; however, this might not be possible during a pandemic. In this paper, a novel data-efficient deep network is proposed for the identification of COVID-19 on CT images. This method increases the small number of available CT scans by generating synthetic versions of CT scans using the generative adversarial network (GAN). Then, we estimate the parameters of convolutional and fully connected layers of the deep networks using synthetic and augmented data. The method shows that the GAN-based deep learning model provides higher performance than classic deep learning models for COVID-19 detection. The performance evaluation is performed on COVID19-CT and Mosmed datasets. The best performing models are ResNet-18 and MobileNetV2 on COVID19-CT and Mosmed, respectively. The area under curve values of ResNet-18 and MobileNetV2 are 0.89% and 0.84%, respectively.
Collapse
|
169
|
Carobene A, Milella F, Famiglini L, Cabitza F. How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data. Clin Chem Lab Med 2022; 60:1887-1901. [PMID: 35508417 DOI: 10.1515/cclm-2022-0182] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/22/2022] [Indexed: 12/13/2022]
Abstract
The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.
Collapse
Affiliation(s)
- Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | | | - Federico Cabitza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy
| |
Collapse
|
170
|
Zhang Y, Chai Y, Hu Z, Xu Z, Li M, Chen X, Yang C, Liu J. Recent Progress on Rapid Lateral Flow Assay-Based Early Diagnosis of COVID-19. Front Bioeng Biotechnol 2022; 10:866368. [PMID: 35592553 PMCID: PMC9111179 DOI: 10.3389/fbioe.2022.866368] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/04/2022] [Indexed: 12/12/2022] Open
Abstract
The outbreak of the coronavirus disease 2019 (COVID-19) has resulted in enormous losses worldwide. Through effective control measures and vaccination, prevention and curbing have proven significantly effective; however, the disease has still not been eliminated. Therefore, it is necessary to develop a simple, convenient, and rapid detection strategy for controlling disease recurrence and transmission. Taking advantage of their low-cost and simple operation, point-of-care test (POCT) kits for COVID-19 based on the lateral flow assay (LFA) chemistry have become one of the most convenient and widely used screening tools for pathogens in hospitals and at home. In this review, we introduce essential features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, compare existing detection methods, and focus on the principles, merits and limitations of the LFAs based on viral nucleic acids, antigens, and corresponding antibodies. A systematic comparison was realized through summarization and analyses, providing a comprehensive demonstration of the LFA technology and insights into preventing and curbing the COVID-19 pandemic.
Collapse
Affiliation(s)
- Ying Zhang
- Central Laboratory, Longgang District People’s Hospital of Shenzhen and The Second Affiliated Hospital of the Chinese University of Hong Kong, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yujuan Chai
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zulu Hu
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zhourui Xu
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Meirong Li
- Central Laboratory, Longgang District People’s Hospital of Shenzhen and The Second Affiliated Hospital of the Chinese University of Hong Kong, Shenzhen, China
| | - Xin Chen
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Chengbin Yang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Jia Liu
- Central Laboratory, Longgang District People’s Hospital of Shenzhen and The Second Affiliated Hospital of the Chinese University of Hong Kong, Shenzhen, China
| |
Collapse
|
171
|
Zhou Y, Shi Y, Lu W, Wan F. Did Artificial Intelligence Invade Humans? The Study on the Mechanism of Patients' Willingness to Accept Artificial Intelligence Medical Care: From the Perspective of Intergroup Threat Theory. Front Psychol 2022; 13:866124. [PMID: 35592172 PMCID: PMC9112914 DOI: 10.3389/fpsyg.2022.866124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 02/23/2022] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence (AI) has become one of the core driving forces for the future development of the medical industry, but patients are skeptical about the use of AI in medical care. Based on the intergroup threat theory (ITT), this study verified that patients would regard AI as an external group, triggering the perceived threat of the external group, which results in avoidance behaviors in the treatment (experiment 1: n = 446) and diagnosis (experiment 2: n = 330) scenarios. The results show that despite AI can provide expert-level accuracy in medical care, patients are still more likely to rely on human doctors and experience more negative emotions as AI is more involved in medical care (experiment 1). Furthermore, patients pay more attention to threats at the individual level related to themselves, such as realistic threats related to privacy issues and symbolic threats related to the neglect of personal characteristics. In contrast, realistic threats and symbolic threats at the group level had less effect on patients in the medical scenario (experiment 2).
Collapse
Affiliation(s)
- Yuwei Zhou
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Yichuan Shi
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Lu
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Fang Wan
- Fudan University Sports Medicine Institute, Shanghai, China
- Department of Sports Medicine and Arthroscopy Surgery, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
172
|
Li CF, Xu YD, Ding XH, Zhao JJ, Du RQ, Wu LZ, Sun WP. MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification. Comput Biol Med 2022; 144:105340. [PMID: 35305504 PMCID: PMC8912982 DOI: 10.1016/j.compbiomed.2022.105340] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/18/2022] [Accepted: 02/20/2022] [Indexed: 12/16/2022]
Abstract
The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.
Collapse
Affiliation(s)
- Cheng-Fan Li
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Yi-Duo Xu
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Xue-Hai Ding
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.
| | - Jun-Juan Zhao
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Rui-Qi Du
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Li-Zhong Wu
- Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Rd, Shanghai, 200111, China
| | - Wen-Ping Sun
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Rd, Shanghai, 200233, China.
| |
Collapse
|
173
|
Li F, Lu X, Yuan J. MHA-CoroCapsule: Multi-Head Attention Routing-Based Capsule Network for COVID-19 Chest X-Ray Image Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1208-1218. [PMID: 34882550 DOI: 10.1109/tmi.2021.3134270] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The outbreak of COVID-19 threatens the lives and property safety of countless people and brings a tremendous pressure to health care systems worldwide. The principal challenge in the fight against this disease is the lack of efficient detection methods. AI-assisted diagnosis based on deep learning can detect COVID-19 cases for chest X-ray images automatically, and also improve the accuracy and efficiency of doctors' diagnosis. However, large scale annotation of chest X-ray images is difficult because of limited resources and heavy burden on the medical system. To meet the challenge, we propose a capsule network model with multi-head attention routing algorithm, called MHA-CoroCapsule, to provide fast and accurate diagnostics for COVID-19 diseases from chest X-ray images. The MHA-CoroCapsule consists of convolutional layers, two capsule layers, and a non-iterative, parameterized multi-head attention routing algorithm is used to quantify the relationship between the two capsule layers. The experiments are performed on a combined dataset constituted by two publicly available datasets including normal, non-COVID pneumonia and COVID-19 images. The model achieves the accuracy of 97.28%, recall of 97.36%, and precision of 97.38% even with a limited number of samples. The experimental results demonstrate that, contrary to the transfer learning and deep feature extraction approaches, the proposed MHA-CoroCapsule has an encouraging performance with fewer trainable parameters and does not require pretraining and plenty of training samples.
Collapse
|
174
|
Fiscal MRC, Treviño V, Treviño LJR, López RO, Cardona Huerta S, Javier Lara-Díaz V, Peña JGT. COVID-19 classification using thermal images. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210328GRR. [PMID: 35585679 PMCID: PMC9116467 DOI: 10.1117/1.jbo.27.5.056003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE There is a scarcity of published research on the potential role of thermal imaging in the remote detection of respiratory issues due to coronavirus disease-19 (COVID-19). This is a comprehensive study that explores the potential of this imaging technology resulting from its convenient aspects that make it highly accessible: it is contactless, noninvasive, and devoid of harmful radiation effects, and it does not require a complicated installation process. AIM We aim to investigate the role of thermal imaging, specifically thermal video, for the identification of SARS-CoV-2-infected people using infrared technology and to explore the role of breathing patterns in different parts of the thorax for the identification of possible COVID-19 infection. APPROACH We used signal moment, signal texture, and shape moment features extracted from five different body regions of interest (whole upper body, chest, face, back, and side) of images obtained from thermal video clips in which optical flow and super-resolution were used. These features were classified into positive and negative COVID-19 using machine learning strategies. RESULTS COVID-19 detection for male models [receiver operating characteristic (ROC) area under the ROC curve (AUC) = 0.605 95% confidence intervals (CI) 0.58 to 0.64] is more reliable than for female models (ROC AUC = 0.577 95% CI 0.55 to 0.61). Overall, thermal imaging is not very sensitive nor specific in detecting COVID-19; the metrics were below 60% except for the chest view from males. CONCLUSIONS We conclude that, although it may be possible to remotely identify some individuals affected by COVID-19, at this time, the diagnostic performance of current methods for body thermal imaging is not good enough to be used as a mass screening tool.
Collapse
Affiliation(s)
| | - Victor Treviño
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
- Tecnologico de Monterrey, The Institute for Obesity Research, Integrative Biology Unit, Monterrey, Nuevo Leon, México
| | | | - Rocio Ortiz López
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
- Tecnologico de Monterrey, The Institute for Obesity Research, Integrative Biology Unit, Monterrey, Nuevo Leon, México
- Tecnologico de Monterrey, Hospital Zambrano Hellion, San Pedro Garza García, Nuevo León, México
| | - Servando Cardona Huerta
- Tecnologico de Monterrey, Hospital Zambrano Hellion, San Pedro Garza García, Nuevo León, México
| | - Victor Javier Lara-Díaz
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
| | - José Gerardo Tamez Peña
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León, México
| |
Collapse
|
175
|
Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis. Inf Sci (N Y) 2022; 592:389-401. [DOI: 10.1016/j.ins.2022.01.062] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/28/2022] [Accepted: 01/30/2022] [Indexed: 12/12/2022]
|
176
|
Aggarwal P, Mishra NK, Fatimah B, Singh P, Gupta A, Joshi SD. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Comput Biol Med 2022; 144:105350. [PMID: 35305501 PMCID: PMC8890789 DOI: 10.1016/j.compbiomed.2022.105350] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/10/2022] [Accepted: 02/22/2022] [Indexed: 12/16/2022]
Abstract
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
Collapse
Affiliation(s)
| | | | - Binish Fatimah
- The Department of ECE, CMR Institute of Technology, Bengaluru, India.
| | - Pushpendra Singh
- The Department of ECE, National Institute of Technology Hamirpur, HP, India.
| | - Anubha Gupta
- The Department of ECE, IIIT-Delhi, Delhi, 110020, India.
| | - Shiv Dutt Joshi
- The Department of EE, Indian Institute of Technology Delhi, Delhi 110016, India.
| |
Collapse
|
177
|
Chen PH, Huang CC, Wu CC, Chen PH, Tripathi A, Wang YL. Saliva-based COVID-19 detection: A rapid antigen test of SARS-CoV-2 nucleocapsid protein using an electrical-double-layer gated field-effect transistor-based biosensing system. SENSORS AND ACTUATORS. B, CHEMICAL 2022; 357:131415. [PMID: 35043033 PMCID: PMC8758198 DOI: 10.1016/j.snb.2022.131415] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 05/20/2023]
Abstract
Facing the unstopped surges of COVID-19, an insufficient capacity of diagnostic testing jeopardizes the control of disease spread. Due to a centralized setting and a long turnaround, real-time reverse transcription polymerase chain reaction (real-time RT-PCR), the gold standard of viral detection, has fallen short in timely reflecting the epidemic status quo during an urgent outbreak. As such, a rapid screening tool is necessitated to help contain the spread of COVID-19 amid the countries where the vaccine implementations have not been widely deployed. In this work, we propose a saliva-based COVID-19 antigen test using the electrical double layer (EDL)-gated field-effect transistor-based biosensor (BioFET). The detection of SARS-CoV-2 nucleocapsid (N) protein is validated with limits of detection (LoDs) of 0.34 ng/mL (7.44 pM) and 0.14 ng/mL (2.96 pM) in 1× PBS and artificial saliva, respectively. The specificity is inspected with types of antigens, exhibiting low cross-reactivity among MERS-CoV, Influenza A virus, and Influenza B virus. This portable system is embedded with Bluetooth communication and user-friendly interfaces that are fully compatible with digital health, feasibly leading to an on-site turnaround, an effective management, and a proactive response taken by medical providers and frontline health workers.
Collapse
Affiliation(s)
- Pin-Hsuan Chen
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Chih-Cheng Huang
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Chia-Che Wu
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Po-Hsuan Chen
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Adarsh Tripathi
- Institute of Molecular Medicine, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Yu-Lin Wang
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| |
Collapse
|
178
|
Chen PH, Huang CC, Wu CC, Chen PH, Tripathi A, Wang YL. Saliva-based COVID-19 detection: A rapid antigen test of SARS-CoV-2 nucleocapsid protein using an electrical-double-layer gated field-effect transistor-based biosensing system. SENSORS AND ACTUATORS. B, CHEMICAL 2022; 357:131415. [PMID: 35043033 DOI: 10.1016/j.snb.2022.131412] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/08/2022] [Accepted: 01/10/2022] [Indexed: 05/27/2023]
Abstract
Facing the unstopped surges of COVID-19, an insufficient capacity of diagnostic testing jeopardizes the control of disease spread. Due to a centralized setting and a long turnaround, real-time reverse transcription polymerase chain reaction (real-time RT-PCR), the gold standard of viral detection, has fallen short in timely reflecting the epidemic status quo during an urgent outbreak. As such, a rapid screening tool is necessitated to help contain the spread of COVID-19 amid the countries where the vaccine implementations have not been widely deployed. In this work, we propose a saliva-based COVID-19 antigen test using the electrical double layer (EDL)-gated field-effect transistor-based biosensor (BioFET). The detection of SARS-CoV-2 nucleocapsid (N) protein is validated with limits of detection (LoDs) of 0.34 ng/mL (7.44 pM) and 0.14 ng/mL (2.96 pM) in 1× PBS and artificial saliva, respectively. The specificity is inspected with types of antigens, exhibiting low cross-reactivity among MERS-CoV, Influenza A virus, and Influenza B virus. This portable system is embedded with Bluetooth communication and user-friendly interfaces that are fully compatible with digital health, feasibly leading to an on-site turnaround, an effective management, and a proactive response taken by medical providers and frontline health workers.
Collapse
Affiliation(s)
- Pin-Hsuan Chen
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Chih-Cheng Huang
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Chia-Che Wu
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Po-Hsuan Chen
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Adarsh Tripathi
- Institute of Molecular Medicine, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| | - Yu-Lin Wang
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
- Institute of Nanoengineering and Microsystems, National Tsing Hua University, Hsinchu 300044, Taiwan (R.O.C.)
| |
Collapse
|
179
|
Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083895] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted to confirm positive COVID-19 RT-PCR tests. Since the very beginning of the pandemic, researchers in the artificial intelligence area have proposed a large number of automatic diagnosing models, hoping to assist radiologists and improve the diagnosing accuracy. However, after two years of development, there are still few models that can actually be applied in real-world scenarios. Numerous problems have emerged in the research of the automated diagnosis of COVID-19. In this paper, we present a systematic review of these diagnosing models. A total of 179 proposed models are involved. First, we compare the medical image modalities (CT or X-ray) for COVID-19 diagnosis from both the clinical perspective and the artificial intelligence perspective. Then, we classify existing methods into two types—image-level diagnosis (i.e., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models). For both types of methods, we define universal model pipelines and analyze the techniques that have been applied in each step of the pipeline in detail. In addition, we also review some commonly adopted public COVID-19 datasets. More importantly, we present an in-depth discussion of the existing automated diagnosis models and note a total of three significant problems: biased model performance evaluation; inappropriate implementation details; and a low reproducibility, reliability and explainability. For each point, we give corresponding recommendations on how we can avoid making the same mistakes and let AI perform better in the next pandemic.
Collapse
|
180
|
Zheng B, Zhu Y, Shi Q, Yang D, Shao Y, Xu T. MA-Net:Mutex attention network for COVID-19 diagnosis on CT images. APPL INTELL 2022; 52:18115-18130. [PMID: 35431458 PMCID: PMC8994185 DOI: 10.1007/s10489-022-03431-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2022] [Indexed: 12/01/2022]
Abstract
COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT–PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images.
Collapse
Affiliation(s)
- BingBing Zheng
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237 China
| | - Yu Zhu
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237 China
- Shanghai Engineering Research Center of Internet of Things for Respiratory Medicine, Shanghai, 200237 China
| | - Qin Shi
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237 China
| | - Dawei Yang
- Shanghai Engineering Research Center of Internet of Things for Respiratory Medicine, Shanghai, 200237 China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032 China
| | - Yanmei Shao
- Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Qingdao University, Qingdao, Shandong 266000 China
| | - Tao Xu
- Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Qingdao University, Qingdao, Shandong 266000 China
| |
Collapse
|
181
|
Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning. Sci Rep 2022; 12:5616. [PMID: 35379856 PMCID: PMC8978501 DOI: 10.1038/s41598-022-09356-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 03/22/2022] [Indexed: 12/29/2022] Open
Abstract
Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: ‘Worse’, ‘Stable’, or ‘Improved’ on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between “Worse” and “Improved” outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic (‘Consolidation’, ‘Lung Lesion’, ‘Pleural effusion’ and ‘Pneumonia’; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between ‘Worse’ and ‘Improved’ cases with a 0.81 (0.74–0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67–0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.
Collapse
|
182
|
Горбачева АМ, Логвинова ОВ, Мокрышева НГ. [Clinical and demographic analysis of telemedicine «doctor-patient» consultations at the Endocrinology Research Centre]. PROBLEMY ENDOKRINOLOGII 2022; 68:4-15. [PMID: 35841163 PMCID: PMC9762544 DOI: 10.14341/probl13088] [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: 04/01/2022] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The COVID-19 pandemic has accelerated the development of telemedicine technologies. Today there is evidence of the successful use of telemedicine in various fields of health care, in particular in endocrinology. At the same time, there is not enough information for effective integration of telemedicine into the management of patients with various endocrinopathies. AIM The aim of this study is a clinical and demographic assessment of the structure of telemedicine consultations (TMC) conducted at the Endocrinology Research Centre in 2020-2021. MATERIALS AND METHODS A single-stage, single-center retrospective study was conducted. The study included all patients who received at least one TMC at the Endocrinology Research Centre in 2020-2021. Clinical and demographic information was analyzed (gender, age of patients, region of residence, ICD-10 code). All patients signed voluntary informed consent for TMC. The obtained data were processed using the Microsoft Office 2013 software package. RESULTS In 2020, 1,548 TMC were held, in 2021 - 4180 TMC. Among adults, women predominated in the structure of referrals (83-86%), among children there is a tendency towards equivalent referrals for boys and girls (in 2021 - 45% and 55%, respectively). The median age of adult patients in 2021 was 38 years [31; 53], among children - 11 years [7; 14]. In 2020, residents of 74 regions of the Russian Federation applied for TMC, in 2021 - of 82 regions. There is a tendency towards the prevalence of patients from the Central, Volga, Southern and North Caucasian federal districts in the TMC structure. Diseases of the thyroid gland predominated in the nosological structure of TMC. CONCLUSION TMC turned out to be in demand in patients with a wide variety of endocrinopathies. It is important to conduct further analysis of both the TMC market and the effectiveness of remote counseling for various nosologies to determine the place of telemedicine in the modern healthcare structure and to introduce TMK into the system of clinical guidelines and programs of territorial compulsory medical insurance funds.
Collapse
Affiliation(s)
- А. М. Горбачева
- Национальный медицинский исследовательский центр эндокринологии
| | - О. В. Логвинова
- Национальный медицинский исследовательский центр эндокринологии
| | - Н. Г. Мокрышева
- Национальный медицинский исследовательский центр эндокринологии
| |
Collapse
|
183
|
de Vente C, Boulogne LH, Venkadesh KV, Sital C, Lessmann N, Jacobs C, Sanchez CI, van Ginneken B. Automated COVID-19 Grading With Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2022; 3:129-138. [PMID: 35582210 PMCID: PMC9014473 DOI: 10.1109/tai.2021.3115093] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/02/2021] [Accepted: 09/18/2021] [Indexed: 11/08/2022]
Abstract
Amidst the ongoing pandemic, the assessment of computed tomography (CT) images for COVID-19 presence can exceed the workload capacity of radiologists. Several studies addressed this issue by automating COVID-19 classification and grading from CT images with convolutional neural networks (CNNs). Many of these studies reported initial results of algorithms that were assembled from commonly used components. However, the choice of the components of these algorithms was often pragmatic rather than systematic and systems were not compared to each other across papers in a fair manner. We systematically investigated the effectiveness of using 3-D CNNs instead of 2-D CNNs for seven commonly used architectures, including DenseNet, Inception, and ResNet variants. For the architecture that performed best, we furthermore investigated the effect of initializing the network with pretrained weights, providing automatically computed lesion maps as additional network input, and predicting a continuous instead of a categorical output. A 3-D DenseNet-201 with these components achieved an area under the receiver operating characteristic curve of 0.930 on our test set of 105 CT scans and an AUC of 0.919 on a publicly available set of 742 CT scans, a substantial improvement in comparison with a previously published 2-D CNN. This article provides insights into the performance benefits of various components for COVID-19 classification and grading systems. We have created a challenge on grand-challenge.org to allow for a fair comparison between the results of this and future research.
Collapse
Affiliation(s)
- Coen de Vente
- Radboud University Medical Center, Donders Institute for Brain, Cognition and BehaviourDepartment of Medical Imaging6525GANijmegenThe Netherlands.,Informatics Institute, Faculty of ScienceUniversity of Amsterdam 1012 WX Amsterdam The Netherlands
| | - Luuk H Boulogne
- Radboud University Medical Center, Radboud Institute for Health SciencesDepartment of Medical Imaging 6525 GA Nijmegen The Netherlands
| | - Kiran Vaidhya Venkadesh
- Radboud University Medical Center, Radboud Institute for Health SciencesDepartment of Medical Imaging 6525 GA Nijmegen The Netherlands
| | - Cheryl Sital
- Radboud University Medical Center, Radboud Institute for Health SciencesDepartment of Medical Imaging 6525 GA Nijmegen The Netherlands
| | - Nikolas Lessmann
- Radboud University Medical Center, Radboud Institute for Health SciencesDepartment of Medical Imaging 6525 GA Nijmegen The Netherlands
| | - Colin Jacobs
- Radboud University Medical Center, Radboud Institute for Health SciencesDepartment of Medical Imaging 6525 GA Nijmegen The Netherlands
| | - Clara I Sanchez
- Informatics Institute, Faculty of ScienceUniversity of Amsterdam 1012 WX Amsterdam The Netherlands
| | - Bram van Ginneken
- Radboud University Medical Center, Radboud Institute for Health SciencesDepartment of Medical Imaging 6525 GA Nijmegen The Netherlands
| |
Collapse
|
184
|
Rahman T, Khandakar A, Abir FF, Faisal MAA, Hossain MS, Podder KK, Abbas TO, Alam MF, Kashem SB, Islam MT, Zughaier SM, Chowdhury MEH. QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model. Comput Biol Med 2022; 143:105284. [PMID: 35180500 PMCID: PMC8839805 DOI: 10.1016/j.compbiomed.2022.105284] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/26/2022] [Accepted: 02/02/2022] [Indexed: 12/31/2022]
Abstract
The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20-25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively.
Collapse
Affiliation(s)
- Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Farhan Fuad Abir
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Ahasan Atick Faisal
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Shafayet Hossain
- Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Kanchon Kanti Podder
- Department of Biomedical Physics & Technology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Tariq O Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha, 26999, Qatar
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, 2713, Qatar
| | - Saad Bin Kashem
- Department of Computing Science, AFG College with the University of Aberdeen, Doha, Qatar
| | - Mohammad Tariqul Islam
- Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Susu M Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, 2713, Qatar
| | | |
Collapse
|
185
|
Sansone NMS, Boschiero MN, Ortega MM, Ribeiro IA, Peixoto AO, Mendes RT, Marson FAL. Severe Acute Respiratory Syndrome by SARS-CoV-2 Infection or Other Etiologic Agents Among Brazilian Indigenous Population: An Observational Study from the First Year of Coronavirus Disease (COVID)-19 Pandemic. LANCET REGIONAL HEALTH. AMERICAS 2022; 8:100177. [PMID: 35018359 PMCID: PMC8739500 DOI: 10.1016/j.lana.2021.100177] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background Indigenous peoples are vulnerable to pandemics, including to the coronavirus disease (COVID)-19, since it causes high mortality and specially, the loss of elderly Indigenous individuals. Methods The epidemiological data of severe acute respiratory syndrome (SARS) by SARS-CoV-2 infection or other etiologic agents (OEA) among Brazilian Indigenous peoples during the first year of COVID-19 pandemic was obtained from a Brazilian Ministry of Health open-access database to perform an observational study. Considering only Indigenous individuals diagnosed with SARS by COVID-19, the epidemiology data were also evaluated as risk of death. The type of sample collection for virus screening, demographic profile, clinical symptoms, comorbidities, and clinical evolution were evaluated. The primary outcome was considered the death in the Brazilian Indigenous individuals and the secondary outcome, the characteristics of Brazilian Indigenous infected by SARS-CoV-2 or OEA, as the need for intensive care unit admission or the need for mechanical ventilation support. The statistical analysis was done using Logistic Regression Model. Alpha of 0.05. Findings A total of 3,122 cases of Indigenous individuals with SARS in Brazil were reported during the first year of the COVID-19 pandemic. Of these, 1,994 were diagnosed with COVID-19 and 730/1,816 (40.2%) of them died. The death rate among individuals with SARS-CoV-2 was three-fold increased when compared to the group of individuals with OEA. Several symptoms (myalgia, loss of smell, and sore throat) and comorbidities (cardiopathy, systemic arterial hypertension, and diabetes mellitus) were more prevalent in the COVID-19 group when compared to Indigenous individuals with OEA. Similar profile was observed considering the risk of death among the Indigenous individuals with COVID-19 who presented several symptoms (oxygen saturation <95%, dyspnea, and respiratory distress) and comorbidities (renal disorders, cardiopathy, and diabetes mellitus). The multivariate analysis was significant in differentiating between the COVID-19-positive and non-COVID-19 patients [X2(7)=65.187; P-value<0.001]. Among the patients’ features, the following contributed in relation to the diagnosis of COVID-19: age [≥43 years-old [y.o.]; OR=1.984 (95%CI=1.480-2.658)]; loss of smell [OR=2.373 (95%CI=1.461-3.854)]; presence of previous respiratory disorders [OR=0.487; 95%CI=0.287-0.824)]; and fever [OR=1.445 (95%CI=1.082-1.929)]. Also, the multivariate analysis was able to predict the risk of death [X2(9)=293.694; P-value<0.001]. Among the patients’ features, the following contributed in relation to the risk of death: male gender [OR=1.507 (95%CI=1.010-2.250)]; age [≥60 y.o.; OR=3.377 (95%CI=2.292-4.974)]; the need for ventilatory support [invasive mechanical ventilation; OR=24.050 (95%CI=12.584-45.962) and non-invasive mechanical ventilation; OR=2.249 (95%CI=1.378-3.671)]; dyspnea [OR=2.053 (95%CI=1.196-3.522)]; oxygen saturation <95% [OR=1.691 (95%CI=1.050-2.723)]; myalgia [OR=0.423 (95%CI=0.191-0.937)]; and the presence of kidney disorders [OR=3.135 (95%CI=1.144-8.539)]. Interpretation The Brazilian Indigenous peoples are in a vulnerable situation during the COVID-19 pandemic and presented an increased risk of death due to COVID-19. Several factors were associated with enhanced risk of death, as male sex, older age (≥60 y.o.), and need for ventilatory support; also, other factors might help to differentiate SARS by COVID-19 or by OEA, as older age (≥43 y.o.), loss of smell, and fever. Funding Fundação de Amparo à Pesquisa do Estado de São Paulo (Foundation for Research Support of the State of São Paulo; #2021/05810-7).
Collapse
Key Words
- %, Percentage
- 95%CI, 95% Confidence Interval
- COVID-19
- COVID-19, Coronavirus Disease (2019)
- Ethnicity
- H1N1, H1N1 Strain of the Flu (Influenzae) virus
- HRCT, High-Resolution Computed Tomography
- ICU, Intensive Care Unit
- Indigenous
- Intensive Care Unit
- MV, Mechanical Ventilation
- NA, Not Applicable
- OEA, Other Etiologic Agents
- OR, Odds Ratio
- Pandemic
- RT-PCR, Real Time-Polymerase Chain Reaction
- Race
- Respiratory Disease
- SAH, Systemic Arterial Hypertension
- SARS, Severe Acute Respiratory Syndrome
- SARS-CoV, Severe Acute Respiratory Syndrome Coronavirus
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2
- SIVEP-Gripe, Information System for Epidemiological Surveillance of Influenza (Sistema de Informação de Vigilância Epidemiológica da Gripe)
- SUS, Sistema Único de Saúde (Brazilian Public Health System)
- Severe Acute Respiratory Syndrome
- Virus
- y.o., Years Old
Collapse
Affiliation(s)
- Nathália M S Sansone
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, SP, Brazil.,Laboratory of Human and Medical Genetics, São Francisco University, Bragança Paulista, SP, Brazil
| | - Matheus N Boschiero
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, SP, Brazil
| | - Manoela M Ortega
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, SP, Brazil.,Laboratory of Human and Medical Genetics, São Francisco University, Bragança Paulista, SP, Brazil
| | - Isadora A Ribeiro
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, SP, Brazil.,Laboratory of Human and Medical Genetics, São Francisco University, Bragança Paulista, SP, Brazil
| | - Andressa O Peixoto
- Laboratory of Translational Medicine, Center for Investigation in Pediatrics, School of Medical Sciences, University of Campinas. Campinas, SP, Brazil
| | - Roberto T Mendes
- Laboratory of Translational Medicine, Center for Investigation in Pediatrics, School of Medical Sciences, University of Campinas. Campinas, SP, Brazil
| | - Fernando A L Marson
- Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, São Francisco University, Bragança Paulista, SP, Brazil.,Laboratory of Human and Medical Genetics, São Francisco University, Bragança Paulista, SP, Brazil
| |
Collapse
|
186
|
Hernández IC, Mohan S, Jowett N. Automated Stain-Free Histomorphometry of Peripheral Nerve by Contrast-Enhancing Techniques and Artificial Intelligence. J Neurosci Methods 2022; 375:109598. [DOI: 10.1016/j.jneumeth.2022.109598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 03/25/2022] [Accepted: 04/08/2022] [Indexed: 10/18/2022]
|
187
|
Feng L, Ma D, Liu F. Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR IN BIOMEDICINE 2022; 35:e4416. [PMID: 33063400 PMCID: PMC8046845 DOI: 10.1002/nbm.4416] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/25/2020] [Accepted: 09/09/2020] [Indexed: 05/08/2023]
Abstract
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.
Collapse
Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
| |
Collapse
|
188
|
Mulrenan C, Rhode K, Fischer BM. A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray. Diagnostics (Basel) 2022; 12:diagnostics12040869. [PMID: 35453917 PMCID: PMC9025113 DOI: 10.3390/diagnostics12040869] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 11/16/2022] Open
Abstract
A COVID-19 diagnosis is primarily determined by RT-PCR or rapid lateral-flow testing, although chest imaging has been shown to detect manifestations of the virus. This article reviews the role of imaging (CT and X-ray), in the diagnosis of COVID-19, focusing on the published studies that have applied artificial intelligence with the purpose of detecting COVID-19 or reaching a differential diagnosis between various respiratory infections. In this study, ArXiv, MedRxiv, PubMed, and Google Scholar were searched for studies using the criteria terms ‘deep learning’, ‘artificial intelligence’, ‘medical imaging’, ‘COVID-19’ and ‘SARS-CoV-2’. The identified studies were assessed using a modified version of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). Twenty studies fulfilled the inclusion criteria for this review. Out of those selected, 11 papers evaluated the use of artificial intelligence (AI) for chest X-ray and 12 for CT. The size of datasets ranged from 239 to 19,250 images, with sensitivities, specificities and AUCs ranging from 0.789–1.00, 0.843–1.00 and 0.850–1.00. While AI demonstrates excellent diagnostic potential, broader application of this method is hindered by the lack of relevant comparators in studies, sufficiently sized datasets, and independent testing.
Collapse
Affiliation(s)
- Ciara Mulrenan
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London WC2R 2LS, UK; (C.M.); (K.R.)
| | - Kawal Rhode
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London WC2R 2LS, UK; (C.M.); (K.R.)
| | - Barbara Malene Fischer
- School of Biomedical Engineering & Imaging Sciences, Kings College London, London WC2R 2LS, UK; (C.M.); (K.R.)
- Rigshospitalet, Department of Clinical Physiology and Nuclear Medicine, Blegdamsvej 9, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
- Correspondence:
| |
Collapse
|
189
|
von Wedel P, Hagist C. Physicians’ preferences and willingness to pay for artificial intelligence-based assistance tools: a discrete choice experiment among german radiologists. BMC Health Serv Res 2022; 22:398. [PMID: 35346183 PMCID: PMC8959781 DOI: 10.1186/s12913-022-07769-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 03/11/2022] [Indexed: 11/30/2022] Open
Abstract
Background Artificial Intelligence (AI)-based assistance tools have the potential to improve the quality of healthcare when adopted by providers. This work attempts to elicit preferences and willingness to pay for these tools among German radiologists. The goal was to generate insights for tool providers and policymakers regarding the development and funding of ideally designed and priced tools. Ultimately, healthcare systems can only benefit from quality enhancing AI when provider adoption is considered. Methods Since there is no established market for AI-based assistance tools in radiology yet, a discrete choice experiment was conducted. Respondents from the two major German professional radiology associations chose between hypothetical tools composed of five attributes and a no-choice option. The attributes included: provider, application, quality impact, time savings and price. A conditional logit model was estimated identifying preferences for attribute levels, the no-choice option, and significant subject-related interaction effects. Results 114 respondents were included for analysis of which 46% were already using an AI-based assistance tool. Average adoption probability for an AI-based tool was 81% (95% CI 77.1% − 84.4%). Radiologists preferred a tool that assists in routine diagnostics performing at above-radiologist-level quality and saves 50% in diagnostics time at a price-point of €3 per study. The provider is not a significant factor in the decisions. Time savings were considered more important than quality improvements (i.e., detecting more anomalies). Conclusions Radiologists are overall willing to invest in AI-based assistance tools. Development, funding, and research regarding these tools should, however, consider providers’ preferences for features of immediate everyday and economic relevance like time savings to optimize adoption. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-07769-x.
Collapse
|
190
|
Afshar P, Rafiee MJ, Naderkhani F, Heidarian S, Enshaei N, Oikonomou A, Babaki Fard F, Anconina R, Farahani K, Plataniotis KN, Mohammadi A. Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network. Sci Rep 2022; 12:4827. [PMID: 35318368 PMCID: PMC8940967 DOI: 10.1038/s41598-022-08796-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/01/2022] [Indexed: 01/01/2023] Open
Abstract
Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.
Collapse
Affiliation(s)
- Parnian Afshar
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
| | - Moezedin Javad Rafiee
- Department of Medicine and Diagnostic Radiology, McGill University Health Center-Research Institute, Montreal, QC, Canada
| | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
| | - Shahin Heidarian
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Nastaran Enshaei
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | | | - Reut Anconina
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), Rockville, MD, USA
| | | | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada.
| |
Collapse
|
191
|
Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area. Diagnostics (Basel) 2022; 12:diagnostics12030738. [PMID: 35328290 PMCID: PMC8946998 DOI: 10.3390/diagnostics12030738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/08/2022] [Accepted: 03/11/2022] [Indexed: 11/17/2022] Open
Abstract
In this study, we first developed an artificial intelligence (AI)-based algorithm for classifying chest computed tomography (CT) images using the coronavirus disease 2019 Reporting and Data System (CO-RADS). Subsequently, we evaluated its accuracy by comparing the calculated scores with those assigned by radiologists with varying levels of experience. This study included patients with suspected SARS-CoV-2 infection who underwent chest CT imaging between February and October 2020 in Japan, a non-endemic area. For each chest CT, the CO-RADS scores, determined by consensus among three experienced chest radiologists, were used as the gold standard. Images from 412 patients were used to train the model, whereas images from 83 patients were tested to obtain AI-based CO-RADS scores for each image. Six independent raters (one medical student, two residents, and three board-certified radiologists) evaluated the test images. Intraclass correlation coefficients (ICC) and weighted kappa values were calculated to determine the inter-rater agreement with the gold standard. The mean ICC and weighted kappa were 0.754 and 0.752 for the medical student and residents (taken together), 0.851 and 0.850 for the diagnostic radiologists, and 0.913 and 0.912 for AI, respectively. The CO-RADS scores calculated using our AI-based algorithm were comparable to those assigned by radiologists, indicating the accuracy and high reproducibility of our model. Our study findings would enable accurate reading, particularly in areas where radiologists are unavailable, and contribute to improvements in patient management and workflow.
Collapse
|
192
|
Gunraj H, Sabri A, Koff D, Wong A. COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning. Front Med (Lausanne) 2022; 8:729287. [PMID: 35360446 PMCID: PMC8960961 DOI: 10.3389/fmed.2021.729287] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 12/31/2021] [Indexed: 01/08/2023] Open
Abstract
The COVID-19 pandemic continues to rage on, with multiple waves causing substantial harm to health and economies around the world. Motivated by the use of computed tomography (CT) imaging at clinical institutes around the world as an effective complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a deep neural network tailored for detection of COVID-19 cases from chest CT images, along with a large curated benchmark dataset comprising 1,489 patient cases as part of the open-source COVID-Net initiative. However, one potential limiting factor is restricted data quantity and diversity given the single nation patient cohort used in the study. To address this limitation, in this study we introduce enhanced deep neural networks for COVID-19 detection from chest CT images which are trained using a large, diverse, multinational patient cohort. We accomplish this through the introduction of two new CT benchmark datasets, the largest of which comprises a multinational cohort of 4,501 patients from at least 16 countries. To the best of our knowledge, this represents the largest, most diverse multinational cohort for COVID-19 CT images in open-access form. Additionally, we introduce a novel lightweight neural network architecture called COVID-Net CT S, which is significantly smaller and faster than the previously introduced COVID-Net CT architecture. We leverage explainability to investigate the decision-making behavior of the trained models and ensure that decisions are based on relevant indicators, with the results for select cases reviewed and reported on by two board-certified radiologists with over 10 and 30 years of experience, respectively. The best-performing deep neural network in this study achieved accuracy, COVID-19 sensitivity, positive predictive value, specificity, and negative predictive value of 99.0%/99.1%/98.0%/99.4%/99.7%, respectively. Moreover, explainability-driven performance validation shows consistency with radiologist interpretation by leveraging correct, clinically relevant critical factors. The results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CT-2 and the associated benchmark datasets will continue to enable researchers, clinicians, and citizen data scientists alike to build upon them.
Collapse
Affiliation(s)
- Hayden Gunraj
- Vision and Image Processing Lab, University of Waterloo, Waterloo, ON, Canada
- *Correspondence: Hayden Gunraj
| | - Ali Sabri
- Department of Radiology, McMaster University, Hamilton, ON, Canada
- Niagara Health System, St. Catharines, ON, Canada
| | - David Koff
- Department of Radiology, McMaster University, Hamilton, ON, Canada
- Hamilton Health Sciences, Hamilton, ON, Canada
| | - Alexander Wong
- Vision and Image Processing Lab, University of Waterloo, Waterloo, ON, Canada
- Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON, Canada
- DarwinAI Corp., Waterloo, ON, Canada
| |
Collapse
|
193
|
Rana A, Singh H, Mavuduru R, Pattanaik S, Rana PS. Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:18129-18153. [PMID: 35282403 PMCID: PMC8901869 DOI: 10.1007/s11042-022-12214-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 01/04/2022] [Accepted: 01/10/2022] [Indexed: 05/28/2023]
Abstract
The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.
Collapse
Affiliation(s)
- Ashish Rana
- Department of Computer Science and Engineering, TIET, Patiala, Punjab India
| | - Harpreet Singh
- Department of Computer Science and Engineering, TIET, Patiala, Punjab India
| | | | - Smita Pattanaik
- Department of Urology and Pharmacology, PGIMER, Chandigarh, India
| | | |
Collapse
|
194
|
Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance. J Clin Med 2022; 11:jcm11051437. [PMID: 35268531 PMCID: PMC8911292 DOI: 10.3390/jcm11051437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 01/08/2023] Open
Abstract
During the coronavirus disease (COVID-19) pandemic, we admitted suspected or confirmed COVID-19 patients to our isolation wards between 2 March 2020 and 4 May 2020, following a well-designed and efficient assessment protocol. We included 217 patients suspected of COVID-19, of which 27 had confirmed COVID-19. The clinical characteristics of these patients were used to train artificial intelligence (AI) models such as support vector machine (SVM), decision tree, random forest, and artificial neural network for diagnosing COVID-19. When analyzing the performance of the models, SVM showed the highest sensitivity (SVM vs. decision tree vs. random forest vs. artificial neural network: 100% vs. 42.86% vs. 28.57% vs. 71.43%), while decision tree and random forest had the highest specificity (SVM vs. decision tree vs. random forest vs. artificial neural network: 88.37% vs. 100% vs. 100% vs. 94.74%) in the diagnosis of COVID-19. With the aid of AI models, physicians may identify COVID-19 patients earlier, even with few baseline data available, and segregate infected patients earlier to avoid hospital cluster infections and to ensure the safety of medical professionals and ordinary patients in the hospital.
Collapse
|
195
|
Adnan N, Khandker SS, Haq A, Chaity MA, Khalek A, Nazim AQ, Kaitsuka T, Tomizawa K, Mie M, Kobatake E, Ahmed S, Ali NAA, Khondoker MU, Haque M, Jamiruddin MR. Detection of SARS-CoV-2 by antigen ELISA test is highly swayed by viral load and sample storage condition. Expert Rev Anti Infect Ther 2022; 20:473-481. [PMID: 34477019 PMCID: PMC8442762 DOI: 10.1080/14787210.2021.1976144] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/31/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Rapid increase in COVID-19 suspected cases has rendered disease diagnosis challenging, mainly depending upon RT-qPCR. Reliable, rapid, and cost-effective diagnostic assays that complement RT-qPCR should be introduced after thoroughly evaluating their performance upon various disease phases, viral load, and sample storage conditions. OBJECTIVE We investigated the correlation of cycle threshold (Ct) value, which implies the viral load and infection phase, and the storage condition of the clinical specimen with the diagnosis of SARS-CoV-2 through our newly developed in-house rapid enzyme-linked immunosorbent assay (ELISA) system. METHOD Naso-oropharyngeal samples of 339 COVID-19 suspected cases were collected and evaluated through RT-qPCR that were stored up to 30 days in different conditions (i.e. -80°C, -20°C and initially at 4°C followed by -80°C). The clinical specimens were evaluated with our in-house ELISA system after finalizing the assay method through checkerboard assay and minimizing the signal/noise ratio. RESULT The ELISA system showed the highest sensitivity (92.9%) for samples with Ct ≤30 and preserving at -80°C temperature. The sensitivity reduced proportionally with increasing Ct value and preserving temperature. However, the specificity ranged between 98.3% and 100%. CONCLUSION The results indicate the necessity of early infection phase diagnosis and lower temperature preservation of samples to perform rapid antigen ELISA tests.
Collapse
Affiliation(s)
- Nihad Adnan
- Department of Microbiology, Jahangirnagar University, Dhaka, Bangladesh
| | - Shahad Saif Khandker
- Department of Research and Development, Gonoshasthaya-RNA Molecular Diagnostic & Research Center, Dhaka, Bangladesh
| | - Ahsanul Haq
- Department of Research and Development, Gonoshasthaya-RNA Molecular Diagnostic & Research Center, Dhaka, Bangladesh
| | | | - Abdul Khalek
- Department of Diagnostic Laboratory, Enam Medical College and Hospital, Dhaka, Bangladesh
| | - Anawarul Quader Nazim
- Department of Diagnostic Laboratory, Enam Medical College and Hospital, Dhaka, Bangladesh
| | - Taku Kaitsuka
- Department of Pharmaceutical Sciences, School of Pharmacy, International University of Health and Welfare, Fukuoka, Japan
| | - Kazuhito Tomizawa
- Department of Molecular Physiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Masayasu Mie
- Department of Life Science and Technology,School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
| | - Eiry Kobatake
- Department of Life Science and Technology,School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
| | - Sohel Ahmed
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Dhaka, Bangladesh
| | - nor Azlina A Ali
- Department of Physical Rehabilitation Sciences, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Kuantan, Malaysia
| | - Mohib Ullah Khondoker
- Department of Community Medicine, Gonoshasthaya Samaj Vittik Medical College, Dhaka, Bangladesh
| | - Mainul Haque
- The Unit of Pharmacology, Faculty of Medicine and Defence Health Universiti Pertahanan, Nasional Malaysia (National Defence University of Malaysia), Kuala Lumpur, Malaysia
| | | |
Collapse
|
196
|
Soltan AAS, Yang J, Pattanshetty R, Novak A, Yang Y, Rohanian O, Beer S, Soltan MA, Thickett DR, Fairhead R, Zhu T, Eyre DW, Clifton DA, Watson A, Bhargav A, Tough A, Rogers A, Shaikh A, Valensise C, Lee C, Otasowie C, Metcalfe D, Agarwal E, Zareh E, Thangaraj E, Pickles F, Kelly G, Tadikamalla G, Shaw G, Tong H, Davies H, Bahra J, Morgan J, Wilson J, Cutteridge J, O'Byrne K, Farache Trajano L, Oliver M, Pikoula M, Mendoza M, Keevil M, Faisal M, Dole N, Deal O, Conway-Jones R, Sattar S, Kundoor S, Shah S, Muthusami V. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. Lancet Digit Health 2022; 4:e266-e278. [PMID: 35279399 PMCID: PMC8906813 DOI: 10.1016/s2589-7500(21)00272-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/22/2021] [Accepted: 11/24/2021] [Indexed: 12/14/2022]
Abstract
Background Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12–24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department. Methods We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC). Findings 72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858–0·881, 95% CI 0·838–0·912, for CURIAL-Lab and 0·836–0·854, 0·814–0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5–85·7, for CURIAL-Lab and 83·5%, 81·8–85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9–71·8) for CURIAL-Lab and 63·6% (63·1–64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7–62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6–88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4–91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32–64), 16 min (26·3%) sooner than with LFDs (61 min, 37–99; log-rank p<0·0001), and 6 h 52 min (90·2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0·0001). Classification performance was high, with sensitivity of 87·5% (95% CI 52·9–97·8), specificity of 85·4% (81·3–88·7), and negative predictive value of 99·7% (98·2–99·9). CURIAL-Rapide correctly excluded infection for 31 (58·5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR. Interpretation Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas. Funding The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.
Collapse
|
197
|
Srivatsan R, Indi PN, Agrahari S, Menon S, Ashok SD. Machine learning based prognostic model and mobile application software platform for predicting infection susceptibility of COVID-19 using healthcare data. RESEARCH ON BIOMEDICAL ENGINEERING 2022. [PMCID: PMC7659405 DOI: 10.1007/s42600-020-00103-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Introduction Methods Results Conclusion
Collapse
Affiliation(s)
- R. Srivatsan
- School of Electronics Engineering, VIT University, Vellore, India
| | - Prithviraj N. Indi
- School of Electronics Engineering, VIT University, Vellore, India
- School of Electrical Engineering, VIT University, Vellore, India
| | - Swapnil Agrahari
- School of Mechanical Engineering, VIT University, Vellore, India
| | - Siddharth Menon
- School of Electronics Engineering, VIT University, Vellore, India
| | - S. Denis Ashok
- Department of Design and Automation, School of Mechanical Engineering, VIT University, Vellore, Tamil Nadu 632014 India
| |
Collapse
|
198
|
Enshaei N, Oikonomou A, Rafiee MJ, Afshar P, Heidarian S, Mohammadi A, Plataniotis KN, Naderkhani F. COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images. Sci Rep 2022; 12:3212. [PMID: 35217712 PMCID: PMC8881477 DOI: 10.1038/s41598-022-06854-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.
Collapse
Affiliation(s)
- Nastaran Enshaei
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | - Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
| | - Moezedin Javad Rafiee
- Department of Medicine and Diagnostic Radiology, McGill University, Montreal, QC, Canada
| | - Parnian Afshar
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | - Shahin Heidarian
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| | | | - Farnoosh Naderkhani
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada
| |
Collapse
|
199
|
|
200
|
Yao HY, Wan WG, Li X. A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022; 2022:10. [PMID: 35194421 PMCID: PMC8830991 DOI: 10.1186/s13634-022-00842-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) is spreading rapidly around the world, resulting in a global pandemic. Imaging techniques such as computed tomography (CT) play an essential role in the diagnosis and treatment of the disease since lung infection or pneumonia is a common complication. However, training a deep network to learn how to diagnose COVID-19 rapidly and accurately in CT images and segment the infected regions like a radiologist is challenging. Since the infectious area is difficult to distinguish manually annotation, the segmentation results are time-consuming. To tackle these problems, we propose an efficient method based on a deep adversarial network to segment the infection regions automatically. Then, the predicted segment results can assist the diagnostic network in identifying the COVID-19 samples from the CT images. On the other hand, a radiologist-like segmentation network provides detailed information of the infectious regions by separating areas of ground-glass, consolidation, and pleural effusion, respectively. Our method can accurately predict the COVID-19 infectious probability and provide lesion regions in CT images with limited training data. Additionally, we have established a public dataset for multitask learning. Extensive experiments on diagnosis and segmentation show superior performance over state-of-the-art methods.
Collapse
Affiliation(s)
- Hai-yan Yao
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Anyang Institute of Technology, Anyang, China
| | - Wang-gen Wan
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xiang Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| |
Collapse
|