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Xu S, Deo RC, Soar J, Barua PD, Faust O, Homaira N, Jaffe A, Kabir AL, Acharya UR. Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107746. [PMID: 37660550 DOI: 10.1016/j.cmpb.2023.107746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
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Affiliation(s)
- Shuting Xu
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; Cogninet Australia, Sydney, NSW 2010, Australia
| | - Ravinesh C Deo
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Business, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, UK
| | - Nusrat Homaira
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia; James P. Grant School of Public Health, Dhaka, Bangladesh
| | - Adam Jaffe
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia
| | | | - U Rajendra Acharya
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; School of Science and Technology, Kumamoto University, Japan
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Xue M, Jia S, Chen L, Huang H, Yu L, Zhu W. CT-based COPD identification using multiple instance learning with two-stage attention. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107356. [PMID: 36682106 DOI: 10.1016/j.cmpb.2023.107356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 12/29/2022] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide. However, COPD remains underdiagnosed globally. Spirometry is currently the primary tool for diagnosing COPD, but it has unneglected difficulties in detecting mild COPD. Chest computed tomography (CT) has been validated for COPD diagnosis and quantification. Whereas many CT-based deep learning approaches have been developed to identify COPD, it remains challenging to characterize CT-based pathological alternations of COPD which are multidimensional and highly spatially heterogeneous, and the diagnosis performance still needs to be improved. METHODS A multiple instance learning (MIL) with two-stage attention (TSA-MIL) is proposed to identify COPD using CT images. Based on transfer learning, a Resnet-50 model pre-trained on natural images is used to extract multicomponent and multidimensional features of COPD abnormalities, in which a pseudo-color method is designed to transfer single-channel CT slices to RGB-like three channels and meanwhile increase the richness of feature representations. To generate more robust attention score for each instance, a two-stage attention module is utilized with the first stage aiming at discovering the key instance while the second stage correcting the attention score for each instance by calculating its average relative distance to the key instances; besides, an instance-level clustering over feature domain is exploited to further improve feature separability and therefore facilitate the subsequent attention module. CT scans, spirometry and demographic data of a total of 800 participants were collected from a large public hospital, with 720 and 80 participants used for model development and evaluation, respectively. In addition, data of 260 participants from another large hospital were also collected for external validation. RESULTS AND CONCLUSIONS The proposed TSA-MIL approach outperforms not only most of the advanced MIL models, but also other up-to-date COPD identification methods, with an accuracy of 0.9200 and an area under curve (AUC) of 0.9544 on the test set, and with an accuracy of 0.8115 and an AUC of 0.8737 on the external validation set without multicenter effect reduction, which is clinically acceptable. Therefore, this approach is promising to be a powerful tool for COPD diagnosis in clinical practice.
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Affiliation(s)
- Mengfan Xue
- School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Zhejiang Lab, Hangzhou, Zhejiang, 311121, China
| | - Shishen Jia
- School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
| | - Ling Chen
- Zhejiang Lab, Hangzhou, Zhejiang, 311121, China
| | | | - Lijuan Yu
- Hainan Cancer Hospital, Haikou, Hainan, 570312, China
| | - Wentao Zhu
- Zhejiang Lab, Hangzhou, Zhejiang, 311121, China.
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Park H, Yun J, Lee SM, Hwang HJ, Seo JB, Jung YJ, Hwang J, Lee SH, Lee SW, Kim N. Deep Learning-based Approach to Predict Pulmonary Function at Chest CT. Radiology 2023; 307:e221488. [PMID: 36786699 DOI: 10.1148/radiol.221488] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services. Materials and Methods In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV1) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV1 percent predicted (hereafter, FVC% and FEV1%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV1/FVC) less than 70% were used to classify participants at high risk. Results A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV1. For the prediction of the respiratory high-risk group, FVC%, FEV1%, and FEV1/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively. Conclusion A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Hyunjung Park
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Jihye Yun
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Sang Min Lee
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Hye Jeon Hwang
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Joon Beom Seo
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Young Ju Jung
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Jeongeun Hwang
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Se Hee Lee
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Sei Won Lee
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Namkug Kim
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
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Lee HC, Chen CY, Lee SJ, Lee MC, Tsai CY, Chen ST, Li YJ. Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks. Comput Biol Med 2022; 146:105584. [PMID: 35551013 DOI: 10.1016/j.compbiomed.2022.105584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 05/01/2022] [Accepted: 05/01/2022] [Indexed: 11/30/2022]
Abstract
Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure. Prior to the onset of atrial fibrillation, most people experience atrial cardiomyopathy which, if effectively managed, can be prevented from progressing to atrial fibrillation. Electrocardiogram (ECG) can show changes in the heartbeats, and is a common and painless tool to detect heart problems. P-waves in exercise ECGs change more drastically than those in regular ECG, and are more effective in the detection of atrial myocardial diseases. In this paper, we propose a deep learning system to help clinicians to early detect if a patient has atrial enlargement or fibrillation. Firstly, a Convolutional Recurrent Neural Network is employed to locate the P-waves in the patient's exercise ECGs taken in the exercise ECG test process. Relevant parameters are then calculated from the located P-waves. Then a Parallel Bi-directional Long Short-Term Memory Network is applied to analyze the obtained parameters and make a diagnosis for the patient. With our proposed deep learning system, the changes of P-waves collected in different phases in the exercise ECG test can be analyzed simultaneously to get more stable and accurate results. The system can take data of different length as input, and is also applicable to any number of ECG collections. We conduct various experiments to show the effectiveness of our proposed system. We also show that the more ECG data collected in the exercise phase are involved, the more effective our system is in diagnosis of the diseases.
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Affiliation(s)
- Hsiang-Chun Lee
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
| | - Chun-Yen Chen
- Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
| | - Shie-Jue Lee
- Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan; Intelligent Electronic Commerce Research Center, National Sun Yat-Sen University, Kaohsiung, Taiwan.
| | - Ming-Chuan Lee
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
| | - Ching-Yi Tsai
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
| | - Su-Te Chen
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
| | - Yu-Ju Li
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
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Exarchos K, Aggelopoulou A, Oikonomou A, Biniskou T, Beli V, Antoniadou E, Kostikas K. Review of Artificial Intelligence techniques in Chronic Obstructive Lung Disease. IEEE J Biomed Health Inform 2021; 26:2331-2338. [PMID: 34914601 DOI: 10.1109/jbhi.2021.3135838] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. OBJECTIVE The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. METHODS We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. RESULTS The resulting articles were assessed and organized into four basic contextual categories, namely: i) COPD diagnosis, ii) COPD prognosis, iii) Patient classification, iv) COPD management, and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. CONCLUSIONS We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.
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Zafari H, Langlois S, Zulkernine F, Kosowan L, Singer A. AI in predicting COPD in the Canadian population. Biosystems 2021; 211:104585. [PMID: 34864143 DOI: 10.1016/j.biosystems.2021.104585] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 11/17/2021] [Accepted: 11/23/2021] [Indexed: 12/12/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that produces non-reversible airflow limitations. Approximately 10% of Canadians aged 35 years or older are living with COPD. Primary care is often the first contact an individual will have with the healthcare system providing acute care, chronic disease management, and services aimed at health maintenance. This study used Electronic Medical Record (EMR) data from primary care clinics in seven provinces across Canada to develop predictive models to identify COPD in the Canadian population. The comprehensive nature of this primary care EMR data containing structured numeric, categorical, hybrid, and unstructured text data, enables the predictive models to capture symptoms of COPD and discriminate it from diseases with similar symptoms. We applied two supervised machine learning models, a Multilayer Neural Networks (MLNN) model and an Extreme Gradient Boosting (XGB) to identify COPD patients. The XGB model achieved an accuracy of 86% in the test dataset compared to 83% achieved by the MLNN. Utilizing feature importance, we identified a set of key symptoms from the EMR for diagnosing COPD, which included medications, health conditions, risk factors, and patient age. Application of this XGB model to primary care structured EMR data can identify patients with COPD from others having similar chronic conditions for disease surveillance, and improve evidence-based care delivery.
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Affiliation(s)
- Hasan Zafari
- School of Computing, Queen's University, Kingston, Ontario, Canada.
| | - Sarah Langlois
- School of Computing, Queen's University, Kingston, Ontario, Canada.
| | | | - Leanne Kosowan
- Department of Family Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
| | - Alexander Singer
- Department of Family Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
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Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease. BMC Pulm Med 2021; 21:321. [PMID: 34654400 PMCID: PMC8518292 DOI: 10.1186/s12890-021-01682-5] [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: 07/28/2021] [Accepted: 09/29/2021] [Indexed: 11/10/2022] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people’s health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. Results This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert–Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. Conclusion This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.
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Abstract
The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems.
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Xu C, Qi S, Feng J, Xia S, Kang Y, Yao Y, Qian W. DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images. Phys Med Biol 2020; 65:145011. [PMID: 32235077 DOI: 10.1088/1361-6560/ab857d] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic obstructive pulmonary disease (COPD), it is still challenging to represent pathological alternations of multiple dimensions and highly spatial heterogeneity. Deep CNN transferred multiple instance learning (DCT-MIL) is proposed to identify COPD via CT images. After the lung is divided into eight sections along the axial direction, one random axial CT image is taken out from each section as one instance. With one instance as the input, the activations of neural layers of AlexNet trained by natural images are extracted as features. After dimension reduction through principle component analysis, features of all instances are input into three MIL methods: Citation k-Nearest-Neighbor (Citation-KNN), multiple instance support vector machine, and expectation-maximization diverse density. Moreover, the performance dependence of the resulted models on the depth of the neural layer where activations are extracted and the number of features is investigated. The proposed DCT-MIL achieves an exceptional performance with an accuracy of 99.29% and area under curve of 0.9826 while using 100 principle components of features extracted from the fourth convolutional layer and Citation-KNN. It outperforms not only DCT-MIL models using other settings and the pre-trained AlexNet with fine-tuning by montages of eight lung CT images, but also other state-of-art methods. Deep CNN transferred multiple instance learning is suited for identification of COPD using CT images. It can help finding subgroups with high risk of COPD from large populations through CT scans ordered doing lung cancer screening.
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Affiliation(s)
- Caiwen Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, People's Republic of China
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10
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Estépar RSJ. Artificial Intelligence in COPD: New Venues to Study a Complex Disease. BARCELONA RESPIRATORY NETWORK REVIEWS 2020; 6:144-160. [PMID: 33521399 PMCID: PMC7842269 DOI: 10.23866/brnrev:2019-0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 09/02/2020] [Indexed: 06/12/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories. Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems. AI has shown impressive results in areas related to automated clinical decision making, radiological interpretation and prognostication. The unique nature of COPD and the accessibility to well-phenotyped populations result in an ideal scenario for AI development. This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD.
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Affiliation(s)
- Raúl San José Estépar
- Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Franssen FME, Alter P, Bar N, Benedikter BJ, Iurato S, Maier D, Maxheim M, Roessler FK, Spruit MA, Vogelmeier CF, Wouters EFM, Schmeck B. Personalized medicine for patients with COPD: where are we? Int J Chron Obstruct Pulmon Dis 2019; 14:1465-1484. [PMID: 31371934 PMCID: PMC6636434 DOI: 10.2147/copd.s175706] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Accepted: 06/05/2019] [Indexed: 12/19/2022] Open
Abstract
Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.
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Affiliation(s)
- Frits ME Franssen
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Peter Alter
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Nadav Bar
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Birke J Benedikter
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
- Department of Medical Microbiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | | | | | - Michael Maxheim
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Fabienne K Roessler
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Martijn A Spruit
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
- REVAL - Rehabilitation Research Center, BIOMED - Biomedical Research Institute, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Emiel FM Wouters
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Bernd Schmeck
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
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12
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Retson TA, Besser AH, Sall S, Golden D, Hsiao A. Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging. J Thorac Imaging 2019; 34:192-201. [PMID: 31009397 PMCID: PMC7962152 DOI: 10.1097/rti.0000000000000385] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.
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Affiliation(s)
- Tara A Retson
- Department of Radiology, University of California San Diego
| | | | | | | | - Albert Hsiao
- Department of Radiology, University of California San Diego
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Bing D, Ying J, Miao J, Lan L, Wang D, Zhao L, Yin Z, Yu L, Guan J, Wang Q. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clin Otolaryngol 2018; 43:868-874. [PMID: 29356346 DOI: 10.1111/coa.13068] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2018] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Sudden sensorineural hearing loss (SSHL) is a multifactorial disorder with high heterogeneity, thus the outcomes vary widely. This study aimed to develop predictive models based on four machine learning methods for SSHL, identifying the best performer for clinical application. DESIGN Single-centre retrospective study. SETTING Chinese People's liberation army (PLA) hospital, Beijing, China. PARTICIPANTS A total of 1220 in-patient SSHL patients were enrolled between June 2008 and December 2015. MAIN OUTCOME MEASURES An advanced deep learning technique, deep belief network (DBN), together with the conventional logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) were developed to predict the dichotomised hearing outcome of SSHL by inputting six feature collections derived from 149 potential predictors. Accuracy, precision, recall, F-score and the area under the receiver operator characteristic curves (ROC-AUC) were exploited to compare the prediction performance of different models. RESULTS Overall the best predictive ability was provided by the DBN model when tested in the raw data set with 149 variables, achieving an accuracy of 77.58% and AUC of 0.84. Nevertheless, DBN yielded inferior performance after feature pruning. In contrast, the LR, SVM and MLP models demonstrated opposite trend as the greatest individual prediction powers were obtained when included merely three variables, with the ROC-AUC ranging from 0.79 to 0.81, and then decreased with the increasing size of input features combinations. CONCLUSIONS With the input of enough features, DBN can be a robust prediction tool for SSHL. But LR is more practical for early prediction in routine clinical application using three readily available variables, that is time elapse between symptom onset and study entry, initial hearing level and audiogram.
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Affiliation(s)
- D Bing
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - J Ying
- Medical Support Center, Chinese PLA General Hospital, Beijing, China
| | - J Miao
- Keele campus, York University, Toronto, Canada
| | - L Lan
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - D Wang
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - L Zhao
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - Z Yin
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - L Yu
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - J Guan
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - Q Wang
- Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
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Wang X, Yang W, Weinreb J, Han J, Li Q, Kong X, Yan Y, Ke Z, Luo B, Liu T, Wang L. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep 2017; 7:15415. [PMID: 29133818 PMCID: PMC5684419 DOI: 10.1038/s41598-017-15720-y] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 10/31/2017] [Indexed: 01/11/2023] Open
Abstract
Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.
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Affiliation(s)
- Xinggang Wang
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Jiefang Road 1095, 430030, Wuhan, China
- School of Electronics Information and Communications, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, Hubei, 430074, China
| | - Wei Yang
- Department of Nutrition and Food Hygiene, MOE Key Lab of Environment, Hubei Key Laboratory of Food Nutrition and Safety, Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, 430030, Wuhan, China
| | - Jeffrey Weinreb
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, 208042, Connecticut, USA
| | - Juan Han
- Department of Maternal and Child and Adolescent & Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, 430030, Wuhan, China
| | - Qiubai Li
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Xiangchuang Kong
- Department of Radiology, Union Hospital, Huazhong University of Science and Technology, Jiefang Road 1277, 430022, Wuhan, China
| | - Yongluan Yan
- School of Electronics Information and Communications, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, Hubei, 430074, China
| | - Zan Ke
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Jiefang Road 1095, 430030, Wuhan, China
| | - Bo Luo
- School of mechanical science and engineering, Huazhong University of Science and Technology, Luoyu Road 1037, 430074, Wuhan, China
| | - Tao Liu
- School of mechanical science and engineering, Huazhong University of Science and Technology, Luoyu Road 1037, 430074, Wuhan, China
| | - Liang Wang
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Jiefang Road 1095, 430030, Wuhan, China.
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science &Technology, Jie-Fang-Da-Dao 1095, Wuhan, 430030, P.R. China.
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