Research progress of deep learning in the auxiliary diagnosis 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.

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1. 董吁钢, 杨杰孚. 左心室肥厚诊断和治疗临床路径中国专家共识2023. 中国循环杂志, 2024, 39(1): 17-28.Dong YG, Yang JF. Chinese expert consensus on the clinical pathway of diagnosis and treatment of left ventricular hypertrophy 2023. Chin J Circ, 2024, 39(1): 17-28.
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4. 汤春光, 王利宏. 高血压患者24 h动态血压昼夜节律与左心室肥厚和心肌缺血及心律失常的关系. 中华高血压杂志, 2017, 25(10): 978-981.Tang CG, Wang LH. Relationship between circadian rhythm of 24 h ambulatory blood pressure and left ventricular hypertrophy, myocardial ischemia and arrhythmia in patients with hypertension. Chin J Hypertens, 2017, 25(10): 978-981.
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.
7. Cirillo C, Matarrese MAG, Monda E, et al. Artificial intelligence for left ventricular hypertrophy detection and differentiation on echocardiography, cardiac magnetic resonance and cardiac computed tomography: A systematic review. Int J Cardiol, 2025, 422: 132979.
8. 张艳, 吴昆华, 范洁, 等. 心脏磁共振成像在左心室肥厚性疾病中的应用. 昆明理工大学学报 (自然科学版), 2020, 45(1): 59-64.Zhang Y, Wu KH, Fan J, et al. Application of cardiac magnetic resonance imaging in left ventricular hypertrophic disease. J Kunming University Sci Technol (Nat Sci), 2020, 45(1): 59-64.
9. 汪小开, 金丽仙, 应夏青. 三维超声心动图评估中度肥厚型心肌病患儿乳头肌、二尖瓣结构和功能的应用价值. 心电与循环, 2023, 42(5): 444-448.Wang XK, Jin LX, Ying XQ. Application value of three-dimensional echocardiography in evaluating the structure and function of papillary muscle and mitral valve in children with moderate hypertrophic cardiomyopathy. Cardiol Circ, 2023, 42(5): 444-448.
10. 马志明, 徐洪洋, 邱鹏, 等. 深度学习在心脏瓣膜病辅助诊断中的研究进展. 中国胸心血管外科临床杂志, 2025. Epub ahead of print.Ma ZM, Xu HY, Qiu P, et al. Research progress of deep learning in auxiliary diagnosis of valvular heart disease. Chin J Clin Thorac Cardiovasc Surg, 2025. Epub ahead of print.
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.
13. Kasa G, Teis A, Juncà G, et al. Clinical and prognostic implications of left ventricular dilatation in heart failure. Eur Heart J Cardiovasc Imaging, 2024, 25(6): 849-856.
14. 袁典, 杜昱峥, 魏德健, 等. 卷积神经网络基于MRI在半月板损伤诊断中的研究进展. 磁共振成像, 2024, 15(3): 223-229.Yuan D, Du YZ, Wei DJ, et al. Research progress of convolutional neural network based on MRI in meniscal injury diagnosis. Chin J Magn Reson Imaging, 2024, 15(3): 223-229.
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. Epub ahead of print.Li GW, Liu J, Cao H, et al. Review of Deep learning in image segmentation of colon polyps. J Frontiers Comput Sci Technol, 2025. Epub ahead of print.
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. Epub ahead of print.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. Epub ahead of print.
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.
18. Rabkin SW. Searching for the best machine learning algorithm for the detection of left ventricular hypertrophy from the ECG: A review. Bioengineering (Basel), 2024, 11(5): 489.
19. Zhao C, Liu Y, Chen W, et al. Multi-scale frequency feature fusion transformer for pediatric echocardiography analysis. Applied Soft Computing, 2025, 173112950-112950.
20. Hwang IC, Choi D, Choi YJ, et al. Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model. Sci Rep, 2022, 12(1): 20998.
21. Yu X, Yao X, Wu B, et al. Using deep learning method to identify left ventricular hypertrophy on echocardiography. Int J Cardiovasc Imaging, 2022, 38(4): 759-769.
22. Holste G, Oikonomou EK, Mortazavi BJ, et al. Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning. Commun Med (Lond), 2024, 4(1): 133.
23. Farhad M, Masud MM, Beg A, et al. A data-efficient zero-shot and few-shot Siamese approach for automated diagnosis of left ventricular hypertrophy. Comput Biol Med, 2023, 163: 107129.
24. Duffy G, Cheng PP, Yuan N, et al. High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning. JAMA Cardiol, 2022, 7(4): 386-395.
25. Xu Z, Yu F, Zhang B, et al. Intelligent diagnosis of left ventricular hypertrophy using transthoracic echocardiography videos. Comput Methods Programs Biomed, 2022, 226: 107182.
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