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- Title
A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance.
- Authors
Chen, Wei-Wen; Kuo, Ling; Lin, Yi-Xun; Yu, Wen-Chung; Tseng, Chien-Chao; Lin, Yenn-Jiang; Huang, Ching-Chun; Chang, Shih-Lin; Wu, Jacky Chung-Hao; Chen, Chun-Ku; Weng, Ching-Yao; Chan, Siwa; Lin, Wei-Wen; Hsieh, Yu-Cheng; Lin, Ming-Chih; Fu, Yun-Ching; Chen, Tsung; Chen, Shih-Ann; Lu, Henry Horng-Shing
- Abstract
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 F 1 -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 F 1 -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.
- Subjects
ANGIOKERATOMA corporis diffusum; CARDIOMYOPATHIES; COMPUTER-assisted image analysis (Medicine); T-test (Statistics); STATISTICAL significance; CARDIAC hypertrophy; MAGNETIC resonance imaging; CHI-squared test; DESCRIPTIVE statistics; DEEP learning; DATA analysis software; HEART ventricles; ECHOCARDIOGRAPHY
- Publication
International Journal of Biomedical Imaging, 2024, Vol 2024, p1
- ISSN
1687-4188
- Publication type
Article
- DOI
10.1155/2024/6114826