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

Autor: Chen WW; Institute of Computer Science and Engineering, National Yang-Ming University, Hsinchu, Taiwan., Kuo L; Faculty of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.; Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan., Lin YX; Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan., Yu WC; Faculty of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan., Tseng CC; Institute of Computer Science and Engineering, National Yang-Ming University, Hsinchu, Taiwan., Lin YJ; Faculty of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan., Huang CC; Institute of Computer Science and Engineering, National Yang-Ming University, Hsinchu, Taiwan., Chang SL; Faculty of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan., Wu JC; Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan., Chen CK; Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan., Weng CY; Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan., Chan S; Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan.; Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan., Lin WW; Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan., Hsieh YC; Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan., Lin MC; Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, Taiwan.; Department of Pediatric Cardiology, Taichung Veterans General Hospital, Taichung, Taiwan.; Children's Medical Center, Taichung Veterans General Hospital, Taichung, Taiwan., Fu YC; Department of Pediatric Cardiology, Taichung Veterans General Hospital, Taichung, Taiwan.; Children's Medical Center, Taichung Veterans General Hospital, Taichung, Taiwan.; Department of Pediatrics, School of Medicine, National Chung-Hsing University, Taichung, Taiwan., Chen T; Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan., Chen SA; Faculty of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.; Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan.; College of Medicine, National Chung Hsing University, Taichung, Taiwan., Lu HH; Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.; Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
Jazyk: angličtina
Zdroj: International journal of biomedical imaging [Int J Biomed Imaging] 2024 Apr 26; Vol. 2024, pp. 6114826. Date of Electronic Publication: 2024 Apr 26 (Print Publication: 2024).
DOI: 10.1155/2024/6114826
Abstrakt: 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.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2024 Wei-Wen Chen et al.)
Databáze: MEDLINE
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