A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance
A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2024/6114826 |
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| author | Wei-Wen Chen Ling Kuo Yi-Xun Lin Wen-Chung Yu Chien-Chao Tseng Yenn-Jiang Lin Ching-Chun Huang Shih-Lin Chang Jacky Chung-Hao Wu Chun-Ku Chen Ching-Yao Weng Siwa Chan Wei-Wen Lin Yu-Cheng Hsieh Ming-Chih Lin Yun-Ching Fu Tsung Chen Shih-Ann Chen Henry Horng-Shing Lu |
| author_facet | Wei-Wen Chen Ling Kuo Yi-Xun Lin Wen-Chung Yu Chien-Chao Tseng Yenn-Jiang Lin Ching-Chun Huang Shih-Lin Chang Jacky Chung-Hao Wu Chun-Ku Chen Ching-Yao Weng Siwa Chan Wei-Wen Lin Yu-Cheng Hsieh Ming-Chih Lin Yun-Ching Fu Tsung Chen Shih-Ann Chen Henry Horng-Shing Lu |
| author_sort | Wei-Wen Chen |
| collection | DOAJ |
| description | A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model’s reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases. |
| format | Article |
| id | doaj-art-3a23b39a0d204852ae7f41e12ea06af8 |
| institution | Kabale University |
| issn | 1687-4196 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-3a23b39a0d204852ae7f41e12ea06af82025-02-03T05:55:21ZengWileyInternational Journal of Biomedical Imaging1687-41962024-01-01202410.1155/2024/6114826A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic ResonanceWei-Wen Chen0Ling Kuo1Yi-Xun Lin2Wen-Chung Yu3Chien-Chao Tseng4Yenn-Jiang Lin5Ching-Chun Huang6Shih-Lin Chang7Jacky Chung-Hao Wu8Chun-Ku Chen9Ching-Yao Weng10Siwa Chan11Wei-Wen Lin12Yu-Cheng Hsieh13Ming-Chih Lin14Yun-Ching Fu15Tsung Chen16Shih-Ann Chen17Henry Horng-Shing Lu18Institute of Computer Science and EngineeringFaculty of Medicine and Institute of Clinical MedicineInstitute of StatisticsFaculty of Medicine and Institute of Clinical MedicineInstitute of Computer Science and EngineeringFaculty of Medicine and Institute of Clinical MedicineInstitute of Computer Science and EngineeringFaculty of Medicine and Institute of Clinical MedicineInstitute of StatisticsDepartment of RadiologyDepartment of RadiologyDepartment of RadiologyCardiovascular CenterCardiovascular CenterDepartment of Pediatric CardiologyDepartment of Pediatric CardiologyInstitute of StatisticsCollege of MedicineDepartment of Statistics and Data ScienceA challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model’s reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.http://dx.doi.org/10.1155/2024/6114826 |
| spellingShingle | Wei-Wen Chen Ling Kuo Yi-Xun Lin Wen-Chung Yu Chien-Chao Tseng Yenn-Jiang Lin Ching-Chun Huang Shih-Lin Chang Jacky Chung-Hao Wu Chun-Ku Chen Ching-Yao Weng Siwa Chan Wei-Wen Lin Yu-Cheng Hsieh Ming-Chih Lin Yun-Ching Fu Tsung Chen Shih-Ann Chen Henry Horng-Shing Lu A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance International Journal of Biomedical Imaging |
| title | A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance |
| title_full | A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance |
| title_fullStr | A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance |
| title_full_unstemmed | A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance |
| title_short | A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance |
| title_sort | deep learning approach to classify fabry cardiomyopathy from hypertrophic cardiomyopathy using cine imaging on cardiac magnetic resonance |
| url | http://dx.doi.org/10.1155/2024/6114826 |
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