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Wang F, Li X, Wen R, Luo H, Liu D, Qi S, Jing Y, Wang P, Deng G, Huang C, Du T, Wang L, Liang H, Wang J, Liu C. Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography. Eur Radiol 2023; 33:8869-8878. [PMID: 37389609 DOI: 10.1007/s00330-023-09833-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVES This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification of bacterial, fungal, and viral pneumonia. METHODS A total of 2763 participants with chest CT images and definite pathogen diagnosis were included to train and validate an algorithm. Pneumonia-Plus was prospectively tested on a nonoverlapping dataset of 173 patients. The algorithm's performance in classifying three types of pneumonia was compared to that of three radiologists using the McNemar test to verify its clinical usefulness. RESULTS Among the 173 patients, area under the curve (AUC) values for viral, fungal, and bacterial pneumonia were 0.816, 0.715, and 0.934, respectively. Viral pneumonia was accurately classified with sensitivity, specificity, and accuracy of 0.847, 0.919, and 0.873. Three radiologists also showed good consistency with Pneumonia-Plus. The AUC values of bacterial, fungal, and viral pneumonia were 0.480, 0.541, and 0.580 (radiologist 1: 3-year experience); 0.637, 0.693, and 0.730 (radiologist 2: 7-year experience); and 0.734, 0.757, and 0.847 (radiologist 3: 12-year experience), respectively. The McNemar test results for sensitivity showed that the diagnostic performance of the algorithm was significantly better than that of radiologist 1 and radiologist 2 (p < 0.05) in differentiating bacterial and viral pneumonia. Radiologist 3 had a higher diagnostic accuracy than the algorithm. CONCLUSIONS The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist and reduce the risk of misdiagnosis. The Pneumonia-Plus is important for appropriate treatment and avoiding the use of unnecessary antibiotics, and provide timely information to guide clinical decision-making and improve patient outcomes. CLINICAL RELEVANCE STATEMENT Pneumonia-Plus algorithm could assist in the accurate classification of pneumonia based on CT images, which has great clinical value in avoiding the use of unnecessary antibiotics, and providing timely information to guide clinical decision-making and improve patient outcomes. KEY POINTS • The Pneumonia-Plus algorithm trained from data collected from multiple centers can accurately identify bacterial, fungal, and viral pneumonia. • The Pneumonia-Plus algorithm was found to have better sensitivity in classifying viral and bacterial pneumonia in comparison to radiologist 1 (5-year experience) and radiologist 2 (7-year experience). • The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist.
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Affiliation(s)
- Fang Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China
| | - Xiaoming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China
| | - Ru Wen
- Medical College, Guizhou University, Guiyang, Guizhou Province, 550000, China
| | - Hu Luo
- No 1. Intensive Care Unit, Huoshenshan Hospital, Wuhan, China
- Department of Respiratory and Critical Care Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Dong Liu
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Shuai Qi
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Yang Jing
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Peng Wang
- Medical Big Data and Artificial Intelligence Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Gang Deng
- Department of Radiology, Maternal and Child Health Hospital of Hubei Province, Guanggu District, Wuhan, China
| | - Cong Huang
- Department of Radiology, The 926 Hospital of PLA, Kaiyuan, China
| | - Tingting Du
- Department of Radiology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Limei Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China
| | - Hongqin Liang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China.
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China.
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China.
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Han D, Chen Y, Li X, Li W, Zhang X, He T, Yu Y, Dou Y, Duan H, Yu N. Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia. LA RADIOLOGIA MEDICA 2023; 128:68-80. [PMID: 36574111 PMCID: PMC9793822 DOI: 10.1007/s11547-022-01580-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 12/13/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE To develop and validate a 3D-convolutional neural network (3D-CNN) model based on chest CT for differentiating active pulmonary tuberculosis (APTB) from community-acquired pneumonia (CAP). MATERIALS AND METHODS Chest CT images of APTB and CAP patients diagnosed in two imaging centers (n = 432 in center A and n = 61 in center B) were collected retrospectively. The data in center A were divided into training, validation and internal test sets, and the data in center B were used as an external test set. A 3D-CNN was built using Keras deep learning framework. After the training, the 3D-CNN selected the model with the highest accuracy in the validation set as the optimal model, which was applied to the two test sets in centers A and B. In addition, the two test sets were independently diagnosed by two radiologists. The 3D-CNN optimal model was compared with the discrimination, calibration and net benefit of the two radiologists in differentiating APTB from CAP using chest CT images. RESULTS The accuracy of the 3D-CNN optimal model was 0.989 and 0.934 with the internal and external test set, respectively. The area-under-the-curve values with the 3D-CNN model in the two test sets were statistically higher than that of the two radiologists (all P < 0.05), and there was a high calibration degree. The decision curve analysis showed that the 3D-CNN optimal model had significantly higher net benefit for patients than the two radiologists. CONCLUSIONS 3D-CNN has high classification performance in differentiating APTB from CAP using chest CT images. The application of 3D-CNN provides a new automatic and rapid diagnosis method for identifying patients with APTB from CAP using chest CT images.
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Affiliation(s)
- Dong Han
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China
| | - Yibing Chen
- School of Information Science & Technology, Northwest University, Xi’an, 710127 Shaanxi China
| | - Xuechao Li
- Clinical Research Center, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Wen Li
- Department of Radiology, Baoji Central Hospital, Baoji, 721008 China
| | - Xirong Zhang
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Taiping He
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Yong Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China ,College of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Yuequn Dou
- Respiratory Department, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712000 China
| | - Haifeng Duan
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000 China
| | - Nan Yu
- Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang West Rd, Xianyang, 712000, China.
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İn E, Altıntop Geçkil A, Kavuran G, Şahin M, Berber NK, Kuluöztürk M. Using Artificial Intelligence to Improve the Diagnostic Efficiency of Pulmonologists in Differentiating COVID-19 Pneumonia from Community-Acquired Pneumonia. J Med Virol 2022; 94:3698-3705. [PMID: 35419818 PMCID: PMC9088454 DOI: 10.1002/jmv.27777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/11/2022] [Indexed: 11/12/2022]
Abstract
Coronavirus disease 2019 (COVID‐19) has quickly turned into a global health problem. Computed tomography (CT) findings of COVID‐19 pneumonia and community‐acquired pneumonia (CAP) may be similar. Artificial intelligence (AI) is a popular topic among medical imaging techniques and has caused significant developments in diagnostic techniques. This retrospective study aims to analyze the contribution of AI to the diagnostic performance of pulmonologists in distinguishing COVID‐19 pneumonia from CAP using CT scans. A deep learning‐based AI model was created to be utilized in the detection of COVID‐19, which extracted visual data from volumetric CT scans. The final data set covered a total of 2496 scans (887 patients), which included 1428 (57.2%) from the COVID‐19 group and 1068 (42.8%) from the CAP group. CT slices were classified into training, validation, and test datasets in an 8:1:1. The independent test data set was analyzed by comparing the performance of four pulmonologists in differentiating COVID‐19 pneumonia both with and without the help of the AI. The accuracy, sensitivity, and specificity values of the proposed AI model for determining COVID‐19 in the independent test data set were 93.2%, 85.8%, and 99.3%, respectively, with the area under the receiver operating characteristic curve of 0.984. With the assistance of the AI, the pulmonologists accomplished a higher mean accuracy (88.9% vs. 79.9%, p < 0.001), sensitivity (79.1% vs. 70%, p < 0.001), and specificity (96.5% vs. 87.5%, p < 0.001). AI support significantly increases the diagnostic efficiency of pulmonologists in the diagnosis of COVID‐19 via CT. Studies in the future should focus on real‐time applications of AI to fight the COVID‐19 infection.
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Affiliation(s)
- Erdal İn
- Department of Pulmonary Medicine, School of Medicine, Malatya Turgut Ozal University, Malatya, Turkey
| | - Ayşegül Altıntop Geçkil
- Department of Pulmonary Medicine, School of Medicine, Malatya Turgut Ozal University, Malatya, Turkey
| | - Gürkan Kavuran
- Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey
| | - Mahmut Şahin
- Department of Radiology, Malatya Training and Research Hospital, Malatya, Turkey
| | - Nurcan Kırıcı Berber
- Department of Pulmonary Medicine, School of Medicine, Malatya Turgut Ozal University, Malatya, Turkey
| | - Mutlu Kuluöztürk
- Department of Pulmonary Medicine, School of Medicine, Firat University, Elazığ, Turkey
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Wang Z, Dong J, Zhang J. Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (SCIENCE) 2022; 27:70-80. [PMID: 34975263 PMCID: PMC8710815 DOI: 10.1007/s12204-021-2392-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 06/07/2021] [Indexed: 11/27/2022]
Abstract
Deep learning based analyses of computed tomography (CT) images contribute to automated diagnosis of COVID-19, and ensemble learning may commonly provide a better solution. Here, we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19. Two ensemble strategies are considered: the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation; voting strategy. A database containing 8 347 CT slices of COVID-19, common pneumonia and normal subjects was used as training and testing sets. Results show that the novel method can reach a high accuracy of 99.37% (recall: 0.9981, precision: 0.989 3), with an increase of about 7% in comparison to single-component models. And the average test accuracy is 95.62% (recall: 0.958 7, precision: 0.955 9), with a corresponding increase of 5.2%. Compared with several latest deep learning models on the identical test set, our method made an accuracy improvement up to 10.88%. The proposed method may be a promising solution for the diagnosis of COVID-19.
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Affiliation(s)
- Zhiming Wang
- College of Electrical Engineering, Sichuan University, Chengdu, 610056 China
| | - Jingjing Dong
- Key Laboratory of Aerospace Medicine of Ministry of Education, Air Force Medical University, Xi’an, 710032 China
- Lintong Rehabilitation and Recuperation Center, PLA Joint Logistic Support Force, Xi’an, 710600 China
| | - Junpeng Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, 610056 China
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5
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Han Y. Artificial intelligence CT helps evaluate the severity of COVID-19 patients: A retrospective study. World J Emerg Med 2022; 13:91-97. [DOI: 10.5847/wjem.j.1920-8642.2022.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/02/2021] [Indexed: 01/08/2023] Open
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Li D, Pehrson LM, Lauridsen CA, Tøttrup L, Fraccaro M, Elliott D, Zając HD, Darkner S, Carlsen JF, Nielsen MB. The Added Effect of Artificial Intelligence on Physicians' Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review. Diagnostics (Basel) 2021; 11:diagnostics11122206. [PMID: 34943442 PMCID: PMC8700414 DOI: 10.3390/diagnostics11122206] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/18/2021] [Accepted: 11/23/2021] [Indexed: 12/20/2022] Open
Abstract
Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.
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Affiliation(s)
- Dana Li
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (L.M.P.); (C.A.L.); (J.F.C.); (M.B.N.)
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
- Correspondence:
| | - Lea Marie Pehrson
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (L.M.P.); (C.A.L.); (J.F.C.); (M.B.N.)
| | - Carsten Ammitzbøl Lauridsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (L.M.P.); (C.A.L.); (J.F.C.); (M.B.N.)
- Department of Technology, Faculty of Health and Technology, University College Copenhagen, 2200 Copenhagen, Denmark
| | - Lea Tøttrup
- Unumed Aps, 1055 Copenhagen, Denmark; (L.T.); (M.F.)
| | | | - Desmond Elliott
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (D.E.); (H.D.Z.); (S.D.)
| | - Hubert Dariusz Zając
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (D.E.); (H.D.Z.); (S.D.)
| | - Sune Darkner
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark; (D.E.); (H.D.Z.); (S.D.)
| | - Jonathan Frederik Carlsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (L.M.P.); (C.A.L.); (J.F.C.); (M.B.N.)
| | - Michael Bachmann Nielsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark; (L.M.P.); (C.A.L.); (J.F.C.); (M.B.N.)
- Department of Clinical Medicine, University of Copenhagen, 2100 Copenhagen, Denmark
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Liu C, Wang Z, Wu W, Xiang C, Wu L, Li J, Hou W, Sun H, Wang Y, Nie Z, Gao Y, Zhang R, Tang H, Wang Q, Li K, Xia X, Li P, Wang S. Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients. Front Med (Lausanne) 2021; 8:699706. [PMID: 34485335 PMCID: PMC8414546 DOI: 10.3389/fmed.2021.699706] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 07/19/2021] [Indexed: 01/08/2023] Open
Abstract
Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.
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Affiliation(s)
- Caidong Liu
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Ziyu Wang
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Wei Wu
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Changgang Xiang
- Department of Laboratory Medicine, First People's Hospital of Jiangxia District of Wuhan, Wuhan, China
| | - Lingxiang Wu
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Jie Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Weiye Hou
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Huiling Sun
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Youli Wang
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhenling Nie
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yingdong Gao
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Ruisheng Zhang
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Haixia Tang
- Department of Critical Care Medicine, Luan Hospital of Chinese Medicine, Lu'an, China
| | - Qianghu Wang
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing, China
| | - Kening Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, China
- Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing, China
| | - Xinyi Xia
- COVID-19 Research Center, Institute of Laboratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
- Department of Laboratory Medicine and Blood Transfusion, Wuhan Huoshenshan Hospital, Wuhan, China
- Joint Expert Group for COVID-19, Wuhan Huoshenshan Hospital, Wuhan, China
| | - Pengping Li
- Department of Bioinformatics, Nanjing Medical University, Nanjing, China
| | - Shukui Wang
- Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Hasan NI. A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE UPDATE 2021; 1:100022. [PMID: 34337590 PMCID: PMC8299229 DOI: 10.1016/j.cmpbup.2021.100022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 07/08/2021] [Accepted: 07/20/2021] [Indexed: 05/02/2023]
Abstract
The outbreak of the SARS-CoV-2/Covid-19 virus in 2019-2020 has made the world look for fast and accurate detection methods of the disease. The most commonly used tools for detecting Covid patients are Chest-X-ray or Chest-CT-scans of the patient. However, sometimes it's hard for the physicians to diagnose the SARS-CoV-2 infection from the raw image. Moreover, sometimes, deep-learning-based techniques, using raw images, fail to detect the infection. Hence, this paper represents a hybrid method employing both traditional signal processing and deep learning technique for quick detection of SARS-CoV-2 patients based on the CT-scan and Chest-X-ray images of a patient. Unlike the other AI-based methods, here, a CT-scan/Chest-X-ray image is decomposed by two-dimensional Empirical Mode Decomposition (2DEMD), and it generates different orders of Intrinsic Mode Functions (IMFs). Next, The decomposed IMF signals are fed into a deep Convolutional Neural Network (CNN) for feature extraction and classification of Covid patients and Non-Covid patients. The proposed method is validated on three publicly available SARS-CoV-2 data sets using two deep CNN architectures. In all the databases, the modified CT-scan/Chest-X-ray image provides a better result than the raw image in terms of classification accuracy of two fundamental CNNs. This paper represents a new viewpoint of extracting preprocessed features from the raw image using 2DEMD.
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Affiliation(s)
- Nahian Ibn Hasan
- Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
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9
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Roth HR, Xu Z, Diez CT, Jacob RS, Zember J, Molto J, Li W, Xu S, Turkbey B, Turkbey E, Yang D, Harouni A, Rieke N, Hu S, Isensee F, Tang C, Yu Q, Sölter J, Zheng T, Liauchuk V, Zhou Z, Moltz JH, Oliveira B, Xia Y, Maier-Hein KH, Li Q, Husch A, Zhang L, Kovalev V, Kang L, Hering A, Vilaça JL, Flores M, Xu D, Wood B, Linguraru MG. Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge. RESEARCH SQUARE 2021:rs.3.rs-571332. [PMID: 34100010 PMCID: PMC8183044 DOI: 10.21203/rs.3.rs-571332/v1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.
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Affiliation(s)
| | | | - Carlos Tor Diez
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA
| | - Ramon Sanchez Jacob
- Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA
| | - Jonathan Zember
- Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA
| | - Jose Molto
- Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA
| | | | - Sheng Xu
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Baris Turkbey
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | | | | | | | - Shishuai Hu
- School of Computer Science and Engineering, Northwestern Polytechnical University, China
| | - Fabian Isensee
- HIP Applied Computer Vision Lab, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Qinji Yu
- Shanghai Jiao Tong University, China
| | - Jan Sölter
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
| | - Tong Zheng
- School of Informatics, Nagoya University, Japan
| | - Vitali Liauchuk
- Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus
| | - Ziqi Zhou
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China
| | | | - Bruno Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yong Xia
- School of Computer Science and Engineering, Northwestern Polytechnical University, China
| | - Klaus H Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Qikai Li
- Shanghai Jiao Tong University, China
| | - Andreas Husch
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | | | - Vassili Kovalev
- Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus
| | - Li Kang
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, China
| | - Alessa Hering
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - João L Vilaça
- 2Ai - Polytechnic Institute of Cávado and Ave, Barcelos, Portugal
| | | | | | - Bradford Wood
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA
- School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
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10
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Goncharov M, Pisov M, Shevtsov A, Shirokikh B, Kurmukov A, Blokhin I, Chernina V, Solovev A, Gombolevskiy V, Morozov S, Belyaev M. CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification. Med Image Anal 2021; 71:102054. [PMID: 33932751 PMCID: PMC8015379 DOI: 10.1016/j.media.2021.102054] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 03/21/2021] [Accepted: 03/26/2021] [Indexed: 12/12/2022]
Abstract
The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset.
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Affiliation(s)
- Mikhail Goncharov
- Skolkovo Institute of Science and Technology, Moscow, Russia; Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | - Maxim Pisov
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Alexey Shevtsov
- Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | - Boris Shirokikh
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Anvar Kurmukov
- Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | - Ivan Blokhin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Russia
| | - Valeria Chernina
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Russia
| | - Alexander Solovev
- Sklifosovsky Clinical and Research Institute for Emergency Medicine, Moscow, Russia
| | - Victor Gombolevskiy
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Russia
| | - Sergey Morozov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Russia
| | - Mikhail Belyaev
- Skolkovo Institute of Science and Technology, Moscow, Russia.
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11
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Xue W, Cao C, Liu J, Duan Y, Cao H, Wang J, Tao X, Chen Z, Wu M, Zhang J, Sun H, Jin Y, Yang X, Huang R, Xiang F, Song Y, You M, Zhang W, Jiang L, Zhang Z, Kong S, Tian Y, Zhang L, Ni D, Xie M. Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information. Med Image Anal 2021; 69:101975. [PMID: 33550007 PMCID: PMC7817458 DOI: 10.1016/j.media.2021.101975] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/17/2020] [Accepted: 01/15/2021] [Indexed: 02/06/2023]
Abstract
The outbreak of COVID-19 around the world has caused great pressure to the health care system, and many efforts have been devoted to artificial intelligence (AI)-based analysis of CT and chest X-ray images to help alleviate the shortage of radiologists and improve the diagnosis efficiency. However, only a few works focus on AI-based lung ultrasound (LUS) analysis in spite of its significant role in COVID-19. In this work, we aim to propose a novel method for severity assessment of COVID-19 patients from LUS and clinical information. Great challenges exist regarding the heterogeneous data, multi-modality information, and highly nonlinear mapping. To overcome these challenges, we first propose a dual-level supervised multiple instance learning module (DSA-MIL) to effectively combine the zone-level representations into patient-level representations. Then a novel modality alignment contrastive learning module (MA-CLR) is presented to combine representations of the two modalities, LUS and clinical information, by matching the two spaces while keeping the discriminative features. To train the nonlinear mapping, a staged representation transfer (SRT) strategy is introduced to maximumly leverage the semantic and discriminative information from the training data. We trained the model with LUS data of 233 patients, and validated it with 80 patients. Our method can effectively combine the two modalities and achieve accuracy of 75.0% for 4-level patient severity assessment, and 87.5% for the binary severe/non-severe identification. Besides, our method also provides interpretation of the severity assessment by grading each of the lung zone (with accuracy of 85.28%) and identifying the pathological patterns of each lung zone. Our method has a great potential in real clinical practice for COVID-19 patients, especially for pregnant women and children, in aspects of progress monitoring, prognosis stratification, and patient management.
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Affiliation(s)
- Wufeng Xue
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, China.
| | - Chunyan Cao
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Jie Liu
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Yilian Duan
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Haiyan Cao
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Jian Wang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, China
| | - Xumin Tao
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, China
| | - Zejian Chen
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, China
| | - Meng Wu
- Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jinxiang Zhang
- Department of Emergency Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Sun
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Jin
- Department of Respiratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Yang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, China
| | - Ruobing Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, China
| | - Feixiang Xiang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Yue Song
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Manjie You
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Wen Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Lili Jiang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Ziming Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Shuangshuang Kong
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Ying Tian
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Li Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China
| | - Dong Ni
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, China.
| | - Mingxing Xie
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, China.
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12
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Yang D, Xu Z, Li W, Myronenko A, Roth HR, Harmon S, Xu S, Turkbey B, Turkbey E, Wang X, Zhu W, Carrafiello G, Patella F, Cariati M, Obinata H, Mori H, Tamura K, An P, Wood BJ, Xu D. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. Med Image Anal 2021; 70:101992. [PMID: 33601166 PMCID: PMC7864789 DOI: 10.1016/j.media.2021.101992] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 12/18/2020] [Accepted: 02/01/2021] [Indexed: 12/23/2022]
Abstract
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing.
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Affiliation(s)
- Dong Yang
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA
| | - Ziyue Xu
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA
| | - Wenqi Li
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA
| | - Andriy Myronenko
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA
| | - Holger R Roth
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA
| | - Stephanie Harmon
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA; Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD USA
| | - Sheng Xu
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, NIH Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Xiaosong Wang
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA
| | - Wentao Zhu
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA
| | - Gianpaolo Carrafiello
- Radiology Department, Fondazione IRCCS Cá Granda Ospedale Maggiore Policlinico, University of Milan, Italy
| | - Francesca Patella
- Diagnostic and Interventional Radiology Service, San Paolo Hospital; ASST Santi Paolo e Carlo, Milan, Italy
| | - Maurizio Cariati
- Diagnostic and Interventional Radiology Service, San Paolo Hospital; ASST Santi Paolo e Carlo, Milan, Italy
| | | | - Hitoshi Mori
- Self-Defense Forces Central Hospital, Tokyo, Japan
| | - Kaku Tamura
- Self-Defense Forces Central Hospital, Tokyo, Japan
| | - Peng An
- Department of Radiology, Xiangyang First People's Hospital Affiliated to Hubei University of Medicine Xiangyang, Hubei, China
| | - Bradford J Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Daguang Xu
- Nvidia Corporation, 4500 East West Highway, Bethesda, Maryland 20814, USA.
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