Progress in the application of deep learning in the auxiliary diagnosis and prognostic evaluation of left ventricular hypertrophy_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

XU Hongyang ¹ , QIU Peng ¹ ,  CAO Hui ¹ , ZHANG Junzhong ² , MA Zhiming ¹

1. School of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, 250355, P. R. China;
2. First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, P. R. China;

Corresponding author：

CAO Hui, Email: caohui6363@163.com

Keywords：

Deep learning; left ventricular hypertrophy; echocardiography; cardiac magnetic resonance imaging; electrocardiogram; prognosis; review

DOI：

10.7507/1007-4848.202503067

Video：

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Abstract Full text Figures/Tables Video References Cited by

As an intermediate phenotype for multiple cardiovascular diseases, left ventricular hypertrophy (LVH) benefits from early diagnosis, which allows for timely intervention to prevent worsening of the condition, mitigate severe complications like heart failure and arrhythmias, and consequently improve patient outcomes. Preliminary advances have been made using deep learning for the early diagnosis and identification of etiology in LVH. This paper reviews the pathophysiology, causes, and diagnostic standards for LVH, discusses the strengths and weaknesses of applying deep learning to diagnostic tools such as echocardiography, cardiac magnetic resonance imaging, and electrocardiogram, examines its use in prognostic evaluation, and concludes by summarizing current achievements and suggesting future research avenues.

Citation： XU Hongyang, QIU Peng, CAO Hui, ZHANG Junzhong, MA Zhiming. Progress in the application of deep learning in the auxiliary diagnosis and prognostic evaluation of left ventricular hypertrophy. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(10): 1495-1503. doi: 10.7507/1007-4848.202503067 Copy

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5. 杨瑶瑶, 邹玉宝. T1 mapping在肥厚型心肌病心肌纤维化评估中的应用进展. 中国分子心脏病学杂志, 2021, 21(4): 4135-4137.Yang YY, Zou YB. Advances in the application of T1 mapping in the evaluation of myocardial fibrosis in hypertrophic cardiomyopathy. Mol Cardiol China, 2021, 21(4): 4135-4137.
6. 冯雪芳, 钟银燕, 章琴莺, 等. 心电图Cornell乘积和Sokolow-Lyon电压与代谢指标关系研究. 中国实用内科杂志, 2018, 38(1): 60-64.Feng XF, Zhong YY, Zhang QY, et al. Relationship between Cornell product and Sokolow-Lyon voltage of electrocardiogram and metabolic indexes. Chin J Pract Intern Med, 2018, 38(1): 60-64.
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11. 王永威, 魏德健, 曹慧, 等. 深度学习在心力衰竭检测中的应用综述. 计算机科学与探索, 2025, 19(1): 65-78.Wang YW, Wei DJ, Cao H, et al. A review of deep learning applications in heart failure detection. J Frontiers Comput Sci Technol, 2025, 19(1): 65-78.
12. 伍倩, 郭辉. 基于深度学习的磁共振成像在冠心病诊疗中的应用进展. 磁共振成像, 2024, 15(11): 190-197.Wu Q, Guo H. Advances in the application of deep learning-based magnetic resonance imaging in coronary heart disease diagnosis and treatment. Chin J Magn Reson Imaging, 2024, 15(11): 190-197.
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15. 李国威, 刘静, 曹慧, 等. 深度学习在结肠息肉图像分割中的研究综述. 计算机科学与探索, 2025, 19(5): 1198-1216.Li GW, Liu J, Cao H, et al. Review of Deep learning in image segmentation of colon polyps. J Frontiers Comput Sci Technol, 2025, 19(5): 1198-1216.
16. 古力米热·阿吾旦, 叶俊翔, 玛依拉·阿不都克力木, 等. 基于深度学习的肺结节CT图像分割与分类研究综述. 计算机工程与应用, 2025, 61(15): 14-35.Gulimire AWD, Ye JX, Mayila ABDKLM, et al. A review of deep learning-based CT image segmentation and classification of pulmonary nodules. Comput Eng Applications, 2025, 61(15): 14-35.
17. 许诗怡, 陈明惠, 邵怡, 等. 结合深度学习的糖尿病视网膜病变血管分割和重建. 中国医学物理学杂志, 2024, 41(10): 1256-1264.Xu SY, Chen MH, Shao Y, et al. Blood vessel segmentation and reconstruction in diabetic retinopathy combined with deep learning. Chin J Med Phys, 2024, 41(10): 1256-1264.
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Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Progress in the application of deep learning in the auxiliary diagnosis and prognostic evaluation of left ventricular hypertrophy

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