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Cheng M, Wang J, Liu X, Wang Y, Wu Q, Wang F, Li P, Wang B, Zhang X, Xie W. Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms. RESEARCH (WASHINGTON, D.C.) 2024; 7:0319. [PMID: 38455153 PMCID: PMC10919123 DOI: 10.34133/research.0319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024]
Abstract
Early detection and treatment of congenital heart disease (CHD) can significantly improve the prognosis of children. However, inexperienced sonographers often face difficulties in recognizing CHD through transthoracic echocardiogram (TTE) images. In this study, 2-dimensional (2D) and Doppler TTEs of children collected from 2 clinical groups from Beijing Children's Hospital between 2018 and 2022 were analyzed, including views of apical 4 chamber, subxiphoid long-axis view of 2 atria, parasternal long-axis view of the left ventricle, parasternal short-axis view of aorta, and suprasternal long-axis view. A deep learning (DL) framework was developed to identify cardiac views, integrate information from various views and modalities, visualize the high-risk region, and predict the probability of the subject being normal or having an atrial septal defect (ASD) or a ventricular septaldefect (VSD). A total of 1,932 children (1,255 healthy controls, 292 ASDs, and 385 VSDs) were collected from 2 clinical groups. For view classification, the DL model reached a mean [SD] accuracy of 0.989 [0.001]. For CHD screening, the model using both 2D and Doppler TTEs with 5 views achieved a mean [SD] area under the receiver operating characteristic curve (AUC) of 0.996 [0.000] and an accuracy of 0.994 [0.002] for within-center evaluation while reaching a mean [SD] AUC of 0.990 [0.003] and an accuracy of 0.993 [0.001] for cross-center test set. For the classification of healthy, ASD, and VSD, the model reached the mean [SD] accuracy of 0.991 [0.002] and 0.986 [0.001] for within- and cross-center evaluation, respectively. The DL models aggregating TTEs with more modalities and scanning views attained superior performance to approximate that of experienced sonographers. The incorporation of multiple views and modalities of TTEs in the model enables accurate identification of children with CHD in a noninvasive manner, suggesting the potential to enhance CHD detection performance and simplify the screening process.
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Affiliation(s)
- Mingmei Cheng
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences,
Anhui Medical University, Hefei 230011, China
| | - Jing Wang
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
- School of Basic Medical Sciences, Capital Medical University, Beijing 10069, China
| | - Xiaofeng Liu
- Gordon Center for Medical Imaging, Harvard Medical School, and Massachusetts General Hospital, Boston, MA 02114, USA
| | - Yanzhong Wang
- School of Life Course and Population Sciences, Faculty of Life Science and Medicine, King’s College London, London, UK
| | - Qun Wu
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Fangyun Wang
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Pei Li
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Binbin Wang
- Center for Genetics,
National Research Institute for Family Planning, Beijing 100730, China
- Graduated School,
Peking Union Medical College, Beijing 100730, China
| | - Xin Zhang
- Heart Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 10045, China
| | - Wanqing Xie
- Department of Intelligent Medical Engineering, School of Biomedical Engineering, Department of Psychology, School of Mental Health and Psychological Sciences,
Anhui Medical University, Hefei 230011, China
- Beth Israel Deaconess Medical Center, Harvard Medical School,
Harvard University, Boston, MA 02215, USA
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Liu X, Xing F, Gaggin HK, Kuo CCJ, El Fakhri G, Woo J. SUCCESSIVE SUBSPACE LEARNING FOR CARDIAC DISEASE CLASSIFICATION WITH TWO-PHASE DEFORMATION FIELDS FROM CINE MRI. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2023; 2023:10.1109/isbi53787.2023.10230746. [PMID: 38031559 PMCID: PMC10686280 DOI: 10.1109/isbi53787.2023.10230746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool. While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples. In addition, many deep learning models are deemed a "black box," for which models remain largely elusive in how models yield a prediction and how reliable they are. To alleviate this, this work proposes a lightweight successive subspace learning (SSL) framework for CVD classification, based on an interpretable feedforward design, in conjunction with a cardiac atlas. Specifically, our hierarchical SSL model is based on (i) neighborhood voxel expansion, (ii) unsupervised subspace approximation, (iii) supervised regression, and (iv) multi-level feature integration. In addition, using two-phase 3D deformation fields, including end-diastolic and end-systolic phases, derived between the atlas and individual subjects as input offers objective means of assessing CVD, even with small training samples. We evaluate our framework on the ACDC2017 database, comprising one healthy group and four disease groups. Compared with 3D CNN-based approaches, our framework achieves superior classification performance with 140× fewer parameters, which supports its potential value in clinical use.
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Affiliation(s)
- Xiaofeng Liu
- Dept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Fangxu Xing
- Dept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Hanna K Gaggin
- Dept. of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - C-C Jay Kuo
- Dept. of ECE, University of Southern California, Los Angeles, CA, USA
| | - Georges El Fakhri
- Dept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jonghye Woo
- Dept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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