利用心脏磁共振成像技术对法布里心肌病和肥厚型心肌病进行分类的深度学习方法。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2024-04-26 eCollection Date: 2024-01-01 DOI:10.1155/2024/6114826
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
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引用次数: 0

摘要

准确识别和分类左心室肥厚(LVH)的一个难题是将其与肥厚型心肌病(HCM)和法布里病区分开来。对成像技术的依赖往往需要多位专家的专业知识,包括心脏病专家、放射科专家和遗传学家。对 LVH 的解释和分类存在差异,导致诊断结果不一致。左心室肥厚、HCM 和法布里心肌病可通过心脏磁共振成像(MRI)的 T1 映射加以区分。然而,对于心脏病专家来说,使用超声心动图或核磁共振成像电影图像区分 HCM 和法布里心肌病具有挑战性。我们提出的核磁共振短轴左心室肥厚分类器(MSLVHC)系统是一个利用人工智能开发的高准确度标准化成像分类模型,并在核磁共振短轴(SAX)视图电影图像上进行训练,以区分 HCM 和法布里病。在台北荣民总医院(TVGH)数据集上进行测试时,该模型取得了令人印象深刻的性能,F1 分数为 0.846,准确率为 0.909,AUC 为 0.914。此外,利用台中荣民总医院(TCVGH)的数据进行的单盲研究和外部测试也证明了该模型的可靠性和有效性,其F1分数为0.727,准确率为0.806,AUC为0.918,证明了该模型的可靠性和实用性。该人工智能模型有望成为协助专家诊断左心室肥大疾病的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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