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Du P, Niu X, Li X, Ying C, Zhou Y, He C, Lv S, Liu X, Du W, Wu W. Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging. BMC Bioinformatics 2023; 24:332. [PMID: 37667214 PMCID: PMC10478337 DOI: 10.1186/s12859-023-05435-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/02/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model. RESULTS The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively. CONCLUSION The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.
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Affiliation(s)
- Peng Du
- Hangzhou AiSmartIoT Co., Ltd., Hangzhou, Zhejiang, China
| | - Xiaofeng Niu
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Xukun Li
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Chiqing Ying
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Yukun Zhou
- Artificial Intelligence Lab, Hangzhou AiSmartVision Co., Ltd., Hangzhou, Zhejiang, China
| | - Chang He
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Shuangzhi Lv
- Department of Radiology The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoli Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China
| | - Weibo Du
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China.
| | - Wei Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 QingChun Road, Hangzhou, 310003, Zhejiang, China.
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Pantazopoulos I, Tsikrika S, Kolokytha S, Manos E, Porpodis K. Management of COVID-19 Patients in the Emergency Department. J Pers Med 2021; 11:jpm11100961. [PMID: 34683102 PMCID: PMC8537207 DOI: 10.3390/jpm11100961] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/15/2022] Open
Abstract
COVID-19 is an emerging disease of global public health concern. As the pandemic overwhelmed emergency departments (EDs), a restructuring of emergency care delivery became necessary in many hospitals. Furthermore, with more than 2000 papers being published each week, keeping up with ever-changing information has proven to be difficult for emergency physicians. The aim of the present review is to provide emergency physician with a summary of the current literature regarding the management of COVID-19 patients in the emergency department.
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Affiliation(s)
- Ioannis Pantazopoulos
- Department of Emergency Medicine, Faculty of Medicine, University of Thessaly, Biopolis, 415 00 Larissa, Greece
- Correspondence: ; Tel.: +30-694-566-1525
| | - Stamatoula Tsikrika
- Emergency Department, Thoracic Diseases COVID-19 Referral Hospital “SOTIRIA”, 115 27 Athens, Greece;
| | - Stavroula Kolokytha
- Department of Emergency Medicine, Sismanoglio Hospital, 151 26 Athens, Greece;
| | - Emmanouil Manos
- Pulmonary Clinic, General Hospital of Lamia, 351 00 Lamia, Greece;
| | - Konstantinos Porpodis
- Respiratory Medicine Department, Aristotle University of Thessaloniki, G Papanikolaou Hospital, 570 10 Thessaloniki, Greece;
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Akdur G, Daş M, Bardakci O, Akman C, Sıddıkoğlu D, Akdur O, Akçalı A, Erbaş M, Reşorlu M, Beyazit Y. Prediction of mortality in COVID-19 through combing CT severity score with NEWS, qSOFA, or peripheral perfusion index. Am J Emerg Med 2021; 50:546-552. [PMID: 34547696 PMCID: PMC8411577 DOI: 10.1016/j.ajem.2021.08.079] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 08/25/2021] [Accepted: 08/30/2021] [Indexed: 10/25/2022] Open
Abstract
INTRODUCTION The assessment of disease severity and the prediction of clinical outcomes at early disease stages can contribute to decreased mortality in patients with Coronavirus disease 2019 (COVID-19). This study was conducted to develop and validate a multivariable risk prediction model for mortality with using a combination of computed tomography severity score (CT-SS), national early warning score (NEWS), and quick sequential (sepsis-related) organ failure assessment (qSOFA) in COVID-19 patients. METHODS We retrospectively collected medical data from 655 adult COVID-19 patients admitted to our hospital between July and November 2020. Data on demographics, clinical characteristics, and laboratory and radiological findings measured as part of standard care at admission were used to calculate NEWS, qSOFA score, CT-SS, peripheral perfusion index (PPI) and shock index (SI). Logistic regression and Cox proportional hazard models were used to predict mortality, which was our primary outcome. The predictive accuracy of distinct scoring systems was evaluated by the receiver-operating characteristic (ROC) curve analysis. RESULTS The median age was 50.0 years [333 males (50.8%), 322 females (49.2%)]. Higher NEWS and SI was associated with time-to-death within 90-days, whereas higher age, CT-SS and lower PPI were significantly associated with time-to-death within both 14 days and 90 days in the adjusted Cox regression model. The CT-SS predicted different mortality risk levels within each stratum of NEWS and qSOFA and improved the discrimination of mortality prediction models. Combining CT-SS with NEWS score yielded more accurate 14 days (DBA: -0.048, p = 0.002) and 90 days (DBA: -0.066, p < 0.001) mortality prediction. CONCLUSION Combining severity tools such as CT-SS, NEWS and qSOFA improves the accuracy of predicting mortality in patients with COVID-19. Inclusion of these tools in decision strategies might provide early detection of high-risk groups, avoid delayed medical attention, and improve patient outcomes.
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Affiliation(s)
- Gökhan Akdur
- Department of Emergency Medicine, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Murat Daş
- Department of Emergency Medicine, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Okan Bardakci
- Department of Emergency Medicine, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey.
| | - Canan Akman
- Department of Emergency Medicine, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Duygu Sıddıkoğlu
- Department of Biostatistics, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Okhan Akdur
- Department of Emergency Medicine, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Alper Akçalı
- Department of Medical Microbiology, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Mesut Erbaş
- Department of Anesthesiology and Reanimation, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Mustafa Reşorlu
- Department of Radiology, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
| | - Yavuz Beyazit
- Department of Internal Medicine, Faculty of Medicine, Çanakkale Onsekiz Mart University, 17020 Çanakkale, Turkey
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Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021; 11:1317. [PMID: 34441252 PMCID: PMC8394327 DOI: 10.3390/diagnostics11081317] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 12/23/2022] Open
Abstract
Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy;
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Alessandro Posa
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario Agostino Gemelli—IRCCS, 00168 Rome, Italy;
| | - Pierandrea Cancian
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Sherif Shalaby
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Emanuele Neri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
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