A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance

Author:

Chen Wei-Wen1ORCID,Kuo Ling234,Lin Yi-Xun5,Yu Wen-Chung23,Tseng Chien-Chao1,Lin Yenn-Jiang23,Huang Ching-Chun1,Chang Shih-Lin23,Wu Jacky Chung-Hao5,Chen Chun-Ku6,Weng Ching-Yao6,Chan Siwa78,Lin Wei-Wen9,Hsieh Yu-Cheng9,Lin Ming-Chih81011,Fu Yun-Ching101112,Chen Tsung5,Chen Shih-Ann23913ORCID,Lu Henry Horng-Shing514ORCID

Affiliation:

1. Institute of Computer Science and Engineering, National Yang-Ming University, Hsinchu, Taiwan

2. Faculty of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

3. Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan

4. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan

5. Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

6. Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan

7. Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan

8. Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan

9. Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan

10. Department of Pediatric Cardiology, Taichung Veterans General Hospital, Taichung, Taiwan

11. Children’s Medical Center, Taichung Veterans General Hospital, Taichung, Taiwan

12. Department of Pediatrics, School of Medicine, National Chung-Hsing University, Taichung, Taiwan

13. College of Medicine, National Chung Hsing University, Taichung, Taiwan

14. Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA

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 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.

Funder

Taipei Veterans General Hospital

Publisher

Hindawi Limited

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