Research progress on deep learning in the assisted diagnosis of valvular heart disease_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

MA Zhiming ¹ , XU Hongyang ¹ , QIU Peng ¹ , LIU Bing ² , JIANG Liang ¹ ,  CAO Hui ¹

1. School of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, 250355, P. R. China;
2. Cardiovascular Medicine, The Fourth People's Hospital of Jinan, Jinan, 250031, P. R. China;

Corresponding author：

CAO Hui, Email: caohui6363@163.com

Keywords：

Deep learning; valvular heart disease; phonocardiogram; electrocardiogram; multimodal fusion; large language model; review

DOI：

10.7507/1007-4848.202410022

Video：

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Abstract Full text Figures/Tables Video References Cited by

Valvular heart disease (VHD) ranks as the third most prevalent cardiovascular disease, following coronary artery disease and hypertension. Severe cases can lead to ventricular hypertrophy or heart failure, highlighting the critical importance of early detection. In recent years, the application of deep learning techniques in the auxiliary diagnosis of VHD has made significant advancements, greatly improving detection accuracy. This review begins by introducing the etiology, pathological mechanisms, and impact of common valvular heart diseases. It then explores the advantages and limitations of using electrocardiographic signals, phonocardiographic signals, and multimodal data in VHD detection. A comparison is made between traditional risk prediction methods and large language models (LLMs) for predicting cardiovascular disease risk, emphasizing the potential of LLMs in risk prediction. Lastly, the current challenges faced by deep learning in this field are discussed, and future research directions are proposed.

1.	国家心血管病中心, 中国心血管健康与疾病报告编写组, 胡盛寿. 中国心血管健康与疾病报告2023概要. 中国循环杂志, 2024, 39(7): 625-660.National Center for Cardiovascular Diseases, Writing Group of Report on Cardiovascular Health and Diseases, Hu SS. Report on Cardiovascular Health and Diseases in China 2023: An updated summary. Chin J Circ, 2024, 39(7): 625-660.
2.	林心平, 王建安. 中国心脏瓣膜病介入治疗的现状与进展. 心电与循环, 2019, 38(5): 353-356.Lin XP, Wang JA. Current status and progress of interventional therapy for heart valve disease in China. Cardiol Circ, 2019, 38(5): 353-356.
3.	Timmis A, Aboyans V, Vardas P, et al. European Society of Cardiology: The 2023 atlas of cardiovascular disease statistics. Eur Heart J, 2024, 45(38): 4019-4062.
4.	Chan V, Malas T, Lapierre H, et al. Reoperation of left heart valve bioprostheses according to age at implantation. Circulation, 2011, 124(11 Suppl): S75-S80.
5.	Mack M, Carroll JD, Thourani V, et al. Transcatheter mitral valve therapy in the United States: A report from the STS-ACC TVT Registry. J Am Coll Cardiol, 2021, 78(23): 2326-2353.
6.	Edler I, Lindström K. The history of echocardiography. Ultrasound Med Biol, 2004, 30(12): 1565-1644.
7.	马宁. 超声心动图用于诊断及治疗儿童心脏瓣膜病进展. 中国医学影像技术, 2024, 40(7): 961-963.Ma N. Progress of echocardiography for diagnosis and treatment of heart valve disease in children. China Med Imaging Technol, 2024, 40(7): 961-963.
8.	Rawshani A, Sattar N, McGuire DK, et al. Left-sided degenerative valvular heart disease in type 1 and type 2 diabetes. Circulation, 2022, 146(5): 398-411.
9.	Bonow RO, Leon MB, Doshi D, et al. Management strategies and future challenges for aortic valve disease. Lancet, 2016, 387(10025): 1312-1323.
10.	Banovic M, Putnik S, Penicka M, et al. Aortic valve replacement versus conservative treatment in asymptomatic severe aortic stenosis: The AVATAR Trial. Circulation, 2022, 145(9): 648-658.
11.	Nishimura RA, Vahanian A, Eleid MF, et al. Mitral valve disease: Current management and future challenges. Lancet, 2016, 387(10025): 1324-1334.
12.	Lim GB. Combining TAVI with PCI in patients with aortic stenosis and CAD. Nat Rev Cardiol, 2024, 21(11): 742.
13.	Hirasawa K, Izumo M, Akashi YJ. Stress echocardiography in valvular heart disease. Front Cardiovasc Med, 2023, 10: 1233924.
14.	Srabanti MG, Adams C, Kadem L, et al. Role of non-invasive hemodynamic forces through four-dimensional-flow magnetic resonance imaging (4D-Flow MRI) in evaluating mitral regurgitation with preserved ejection fraction: Seeking novel biomarkers. Appl Sci, 2024, 14(19): 8577.
15.	Patel KP, Vandermolen S, Herrey AS, et al. Cardiac computed tomography: Application in valvular heart disease. Front Cardiovasc Med, 2022, 9: 849540.
16.	Singh S, Chaudhary R, Bliden KP, et al. Meta-analysis of the performance of AI-driven ECG interpretation in the diagnosis of valvular heart diseases. Am J Cardiol, 2024, 213: 126-131.
17.	Torre-Cruz J, Canadas-Quesada F, Ruiz-Reyes N, et al. Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals. J Biomed Inform, 2023, 145: 104475.
18.	Friedrich S, Groß S, König IR, et al. Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: A systematic review with recommendations. Eur Heart J Digit Health, 2021, 2(3): 424-436.
19.	Wang Z, Qian K, Liu H, et al. Exploring interpretable representations for heart sound abnormality detection. Biomed Signal Process Control, 2023, 82: 104569.
20.	Wang L, Wang H, Huang Y, et al. Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020. Eur J Radiol, 2022, 146: 110069.
21.	Yang Y, Wang Z, Chen Z, et al. Current status and etiology of valvular heart disease in China: A population-based survey. BMC Cardiovasc Disord, 2021, 21(1): 339.
22.	Greco E, Vinciguerra M, Sampaio R, et al. Editorial: New insights in heart valve disease 2022. Front Cardiovasc Med, 2023, 10: 1226113.
23.	Elias P, Poterucha TJ, Rajaram V, et al. Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol, 2022, 80(6): 613-626.
24.	Kim K, Ko YG, Shim CY, et al. Impact of new-onset persistent left bundle branch block on reverse cardiac remodeling and clinical outcomes after transcatheter aortic valve replacement. Front Cardiovasc Med, 2022, 9: 893878.
25.	Kwon JM, Lee SY, Jeon KH, et al. Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc, 2020, 9(7): e014717.
26.	Cohen-Shelly M, Attia ZI, Friedman PA, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J, 2021, 42(30): 2885-2896.
27.	Omar M. For the FITs: Time to embrace the future: AI-assisted ECG detection of VHD, SHD. Cardiol Mag, 2024, 3(29): 1-6.
28.	Parlato S, Muto V, Bifulco P. A novel approach to recognize valvular heart diseases based on morphological similarity of heartbeats in seismocardiography signals. International Conference on e-Health and Bioengineering. Cham: Springer Nature Switzerland, 2023: 188-195.
29.	Chung CT, Lee S, King E, et al. Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis. Int J Arrhythmia, 2022, 23(1): 24.
30.	Zeng W, Lin Z, Yuan C, et al. Detection of heart valve disorders from PCG signals using TQWT, FA-MVEMD, Shannon energy envelope and deterministic learning. Artif Intell Rev, 2021, 2021: 1-38.
31.	Yaseen, Son GY, Kwon S. Classification of heart sound signal using multiple features. Appl Sci, 2018, 8(12): 2344.
32.	Al-Issa Y, Alqudah AM. A lightweight hybrid deep learning system for cardiac valvular disease classification. Sci Rep, 2022, 12(1): 14297.
33.	Bhardwaj A, Singh S, Joshi D. Explainable deep convolutional neural network for valvular heart diseases classification using PCG signals. IEEE trans Instrum Meas, 2023, 72: 1-15.
34.	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. https://doi.org/10.48550/arXiv.1706.03762.
35.	Abbas Q, Hussain A, Baig AR. Automatic detection and classification of cardiovascular disorders using phonocardiogram and convolutional vision transformers. Diagnostics (Basel), 2022, 12(12): 3109.
36.	Jamil S, Roy AM. An efficient and robust phonocardiography (PCG)-based valvular heart diseases (VHD) detection framework using vision transformer (ViT). Comput Biol Med, 2023, 158: 106734.
37.	Bouktif S, Fiaz A, Ouni A, et al. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 2018, 11(7): 1636.
38.	Makimoto H, Shiraga T, Kohlmann B, et al. Efficient screening for severe aortic valve stenosis using understandable artificial intelligence: A prospective diagnostic accuracy study. Eur Heart J Digit Health, 2022, 3(2): 141-152.
39.	Lee SY, Su PH, Hsieh YT, et al. Intelligent stethoscope system and diagnosis platform with synchronized heart sound and electrocardiogram signals. IEEE Access, 2023, 11: 47420-47431.
40.	Shiraga T, Makimoto H, Kohlmann B, et al. Improving valvular pathologies and ventricular dysfunction diagnostic efficiency using combined auscultation and electrocardiography data: A multimodal AI approach. Sensors (Basel), 2023, 23(24): 9834.
41.	Soto JT, Weston Hughes J, Sanchez PA, et al. Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy. Eur Heart J Digit Health, 2022, 3(3): 380-389.
42.	Zhao Q, Geng S, Wang B, et al. Deep learning for heart sound analysis: A literature review. MedRxiv, 2023. https://doi.org/10.1101/2023.09.16.23295653.
43.	Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv, 2016. https://doi.org/10.48550/arXiv.1609.02907.
44.	Habashi AG, Azab AM, Eldawlatly S, et al. Generative adversarial networks in EEG analysis: An overview. J Neuroeng Rehabil, 2023, 20(1): 40.
45.	Ivantsits M, Tautz L, Huellebrand M, et al. MV-GNN: Generation of continuous geometric representations of mitral valve motion from 3D+T echocardiography. Comput Biol Med, 2024, 182: 109154.
46.	Thomas S, Tiago C, Andreassen BS, et al. Graph convolutional neural networks for automated echocardiography view recognition: A holistic approach. International Workshop on Advances in Simplifying Medical Ultrasound. Cham: Springer Nature Switzerland, 2023: 44-54.
47.	Rayavarapu SM, Prasanthi TS, Kumar GS, et al. A generative model for deep fake augmentation of phonocardiogram and electrocardiogram signals using LSGAN and cycle GAN. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2023, 13(4): 34-38.
48.	Vinay NA, Vidyasagar KN, Rohith S, et al. An RNN-Bi LSTM based multi decision GAN approach for the recognition of cardiovascular disease (CVD) from heart beat sound: A feature optimization process. IEEE Access, 2024, 12: 65482-65502.
49.	Zheng C, Peng W, Huang T, et al. High accurate detection method for aortic valve opening of seismocardiography signals. Biomed Signal Process Control, 2024, 87: 105484.
50.	Sieciński S, Tkacz EJ, Kostka PS. Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms of healthy volunteers and patients with valvular heart diseases. Sensors (Basel), 2023, 23(4): 2152.
51.	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 in heart failure detection. Comp Sci Explor, 2025, 19(1): 65-78.
52.	Wells PS, Anderson DR, Rodger M, et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: Management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med, 2001, 135(2): 98-107.
53.	Seymour CW, Liu VX, Iwashyna TJ, et al. Assessment of clinical criteria for sepsis: For the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 2016, 315(8): 762-774.
54.	Namasivayam M, Myers PD, Guttag JV, et al. Predicting outcomes in patients with aortic stenosis using machine learning: The Aortic Stenosis Risk (ASteRisk) score. Open Heart, 2022, 9(1): e001990.
55.	Baker N, Thompson B, Fox D. ChatGPT can write a paper in an hour-but there are downsides. Nature. (2023-07-12) [2024-09-12]. https://www.nature.com/articles/d41586-023-02298-x.
56.	DeLong KA, Trott S, Kutas M. Offline dominance and zeugmatic similarity normings of variably ambiguous words assessed against a neural language model (BERT). Behav Res Methods, 2023, 55(4): 1537-1557.
57.	Sarraju A, Ouyang D, Itchhaporia D. The opportunities and challenges of large language models in cardiology. JACC Adv, 2023, 2(7): 100438.
58.	Vaid A, Duong SQ, Lampert J, et al. Local large language models for privacy-preserving accelerated review of historic echocardiogram reports. J Am Med Inform Assoc, 2024, 31(9): 2097-2102.
59.	Kwon T, Ong KT, Kang D, et al. Large language models are clinical reasoners: Reasoning-aware diagnosis framework with prompt-generated rationales. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(16): 18417-18425.
60.	Novak A, Rode F, Lisičić A, et al. The pulse of artificial intelligence in cardiology: A comprehensive evaluation of state-of-the-art large language models for potential use in clinical cardiology. MedRxiv, 2023. https://doi.org/10.1101/2023.08.08.23293689.
61.	Quer G, Topol EJ. The potential for large language models to transform cardiovascular medicine. Lancet Digit Health, 2024, 6(10): e767-e771.

1. 国家心血管病中心, 中国心血管健康与疾病报告编写组, 胡盛寿. 中国心血管健康与疾病报告2023概要. 中国循环杂志, 2024, 39(7): 625-660.National Center for Cardiovascular Diseases, Writing Group of Report on Cardiovascular Health and Diseases, Hu SS. Report on Cardiovascular Health and Diseases in China 2023: An updated summary. Chin J Circ, 2024, 39(7): 625-660.
2. 林心平, 王建安. 中国心脏瓣膜病介入治疗的现状与进展. 心电与循环, 2019, 38(5): 353-356.Lin XP, Wang JA. Current status and progress of interventional therapy for heart valve disease in China. Cardiol Circ, 2019, 38(5): 353-356.
3. Timmis A, Aboyans V, Vardas P, et al. European Society of Cardiology: The 2023 atlas of cardiovascular disease statistics. Eur Heart J, 2024, 45(38): 4019-4062.
4. Chan V, Malas T, Lapierre H, et al. Reoperation of left heart valve bioprostheses according to age at implantation. Circulation, 2011, 124(11 Suppl): S75-S80.
5. Mack M, Carroll JD, Thourani V, et al. Transcatheter mitral valve therapy in the United States: A report from the STS-ACC TVT Registry. J Am Coll Cardiol, 2021, 78(23): 2326-2353.
6. Edler I, Lindström K. The history of echocardiography. Ultrasound Med Biol, 2004, 30(12): 1565-1644.
7. 马宁. 超声心动图用于诊断及治疗儿童心脏瓣膜病进展. 中国医学影像技术, 2024, 40(7): 961-963.Ma N. Progress of echocardiography for diagnosis and treatment of heart valve disease in children. China Med Imaging Technol, 2024, 40(7): 961-963.
8. Rawshani A, Sattar N, McGuire DK, et al. Left-sided degenerative valvular heart disease in type 1 and type 2 diabetes. Circulation, 2022, 146(5): 398-411.
9. Bonow RO, Leon MB, Doshi D, et al. Management strategies and future challenges for aortic valve disease. Lancet, 2016, 387(10025): 1312-1323.
10. Banovic M, Putnik S, Penicka M, et al. Aortic valve replacement versus conservative treatment in asymptomatic severe aortic stenosis: The AVATAR Trial. Circulation, 2022, 145(9): 648-658.
11. Nishimura RA, Vahanian A, Eleid MF, et al. Mitral valve disease: Current management and future challenges. Lancet, 2016, 387(10025): 1324-1334.
12. Lim GB. Combining TAVI with PCI in patients with aortic stenosis and CAD. Nat Rev Cardiol, 2024, 21(11): 742.
13. Hirasawa K, Izumo M, Akashi YJ. Stress echocardiography in valvular heart disease. Front Cardiovasc Med, 2023, 10: 1233924.
14. Srabanti MG, Adams C, Kadem L, et al. Role of non-invasive hemodynamic forces through four-dimensional-flow magnetic resonance imaging (4D-Flow MRI) in evaluating mitral regurgitation with preserved ejection fraction: Seeking novel biomarkers. Appl Sci, 2024, 14(19): 8577.
15. Patel KP, Vandermolen S, Herrey AS, et al. Cardiac computed tomography: Application in valvular heart disease. Front Cardiovasc Med, 2022, 9: 849540.
16. Singh S, Chaudhary R, Bliden KP, et al. Meta-analysis of the performance of AI-driven ECG interpretation in the diagnosis of valvular heart diseases. Am J Cardiol, 2024, 213: 126-131.
17. Torre-Cruz J, Canadas-Quesada F, Ruiz-Reyes N, et al. Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals. J Biomed Inform, 2023, 145: 104475.
18. Friedrich S, Groß S, König IR, et al. Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: A systematic review with recommendations. Eur Heart J Digit Health, 2021, 2(3): 424-436.
19. Wang Z, Qian K, Liu H, et al. Exploring interpretable representations for heart sound abnormality detection. Biomed Signal Process Control, 2023, 82: 104569.
20. Wang L, Wang H, Huang Y, et al. Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020. Eur J Radiol, 2022, 146: 110069.
21. Yang Y, Wang Z, Chen Z, et al. Current status and etiology of valvular heart disease in China: A population-based survey. BMC Cardiovasc Disord, 2021, 21(1): 339.
22. Greco E, Vinciguerra M, Sampaio R, et al. Editorial: New insights in heart valve disease 2022. Front Cardiovasc Med, 2023, 10: 1226113.
23. Elias P, Poterucha TJ, Rajaram V, et al. Deep learning electrocardiographic analysis for detection of left-sided valvular heart disease. J Am Coll Cardiol, 2022, 80(6): 613-626.
24. Kim K, Ko YG, Shim CY, et al. Impact of new-onset persistent left bundle branch block on reverse cardiac remodeling and clinical outcomes after transcatheter aortic valve replacement. Front Cardiovasc Med, 2022, 9: 893878.
25. Kwon JM, Lee SY, Jeon KH, et al. Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc, 2020, 9(7): e014717.
26. Cohen-Shelly M, Attia ZI, Friedman PA, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J, 2021, 42(30): 2885-2896.
27. Omar M. For the FITs: Time to embrace the future: AI-assisted ECG detection of VHD, SHD. Cardiol Mag, 2024, 3(29): 1-6.
28. Parlato S, Muto V, Bifulco P. A novel approach to recognize valvular heart diseases based on morphological similarity of heartbeats in seismocardiography signals. International Conference on e-Health and Bioengineering. Cham: Springer Nature Switzerland, 2023: 188-195.
29. Chung CT, Lee S, King E, et al. Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis. Int J Arrhythmia, 2022, 23(1): 24.
30. Zeng W, Lin Z, Yuan C, et al. Detection of heart valve disorders from PCG signals using TQWT, FA-MVEMD, Shannon energy envelope and deterministic learning. Artif Intell Rev, 2021, 2021: 1-38.
31. Yaseen, Son GY, Kwon S. Classification of heart sound signal using multiple features. Appl Sci, 2018, 8(12): 2344.
32. Al-Issa Y, Alqudah AM. A lightweight hybrid deep learning system for cardiac valvular disease classification. Sci Rep, 2022, 12(1): 14297.
33. Bhardwaj A, Singh S, Joshi D. Explainable deep convolutional neural network for valvular heart diseases classification using PCG signals. IEEE trans Instrum Meas, 2023, 72: 1-15.
34. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. https://doi.org/10.48550/arXiv.1706.03762.
35. Abbas Q, Hussain A, Baig AR. Automatic detection and classification of cardiovascular disorders using phonocardiogram and convolutional vision transformers. Diagnostics (Basel), 2022, 12(12): 3109.
36. Jamil S, Roy AM. An efficient and robust phonocardiography (PCG)-based valvular heart diseases (VHD) detection framework using vision transformer (ViT). Comput Biol Med, 2023, 158: 106734.
37. Bouktif S, Fiaz A, Ouni A, et al. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 2018, 11(7): 1636.
38. Makimoto H, Shiraga T, Kohlmann B, et al. Efficient screening for severe aortic valve stenosis using understandable artificial intelligence: A prospective diagnostic accuracy study. Eur Heart J Digit Health, 2022, 3(2): 141-152.
39. Lee SY, Su PH, Hsieh YT, et al. Intelligent stethoscope system and diagnosis platform with synchronized heart sound and electrocardiogram signals. IEEE Access, 2023, 11: 47420-47431.
40. Shiraga T, Makimoto H, Kohlmann B, et al. Improving valvular pathologies and ventricular dysfunction diagnostic efficiency using combined auscultation and electrocardiography data: A multimodal AI approach. Sensors (Basel), 2023, 23(24): 9834.
41. Soto JT, Weston Hughes J, Sanchez PA, et al. Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy. Eur Heart J Digit Health, 2022, 3(3): 380-389.
42. Zhao Q, Geng S, Wang B, et al. Deep learning for heart sound analysis: A literature review. MedRxiv, 2023. https://doi.org/10.1101/2023.09.16.23295653.
43. Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv, 2016. https://doi.org/10.48550/arXiv.1609.02907.
44. Habashi AG, Azab AM, Eldawlatly S, et al. Generative adversarial networks in EEG analysis: An overview. J Neuroeng Rehabil, 2023, 20(1): 40.
45. Ivantsits M, Tautz L, Huellebrand M, et al. MV-GNN: Generation of continuous geometric representations of mitral valve motion from 3D+T echocardiography. Comput Biol Med, 2024, 182: 109154.
46. Thomas S, Tiago C, Andreassen BS, et al. Graph convolutional neural networks for automated echocardiography view recognition: A holistic approach. International Workshop on Advances in Simplifying Medical Ultrasound. Cham: Springer Nature Switzerland, 2023: 44-54.
47. Rayavarapu SM, Prasanthi TS, Kumar GS, et al. A generative model for deep fake augmentation of phonocardiogram and electrocardiogram signals using LSGAN and cycle GAN. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2023, 13(4): 34-38.
48. Vinay NA, Vidyasagar KN, Rohith S, et al. An RNN-Bi LSTM based multi decision GAN approach for the recognition of cardiovascular disease (CVD) from heart beat sound: A feature optimization process. IEEE Access, 2024, 12: 65482-65502.
49. Zheng C, Peng W, Huang T, et al. High accurate detection method for aortic valve opening of seismocardiography signals. Biomed Signal Process Control, 2024, 87: 105484.
50. Sieciński S, Tkacz EJ, Kostka PS. Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms of healthy volunteers and patients with valvular heart diseases. Sensors (Basel), 2023, 23(4): 2152.
51. 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 in heart failure detection. Comp Sci Explor, 2025, 19(1): 65-78.
52. Wells PS, Anderson DR, Rodger M, et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: Management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med, 2001, 135(2): 98-107.
53. Seymour CW, Liu VX, Iwashyna TJ, et al. Assessment of clinical criteria for sepsis: For the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA, 2016, 315(8): 762-774.
54. Namasivayam M, Myers PD, Guttag JV, et al. Predicting outcomes in patients with aortic stenosis using machine learning: The Aortic Stenosis Risk (ASteRisk) score. Open Heart, 2022, 9(1): e001990.
55. Baker N, Thompson B, Fox D. ChatGPT can write a paper in an hour-but there are downsides. Nature. (2023-07-12) [2024-09-12]. https://www.nature.com/articles/d41586-023-02298-x.
56. DeLong KA, Trott S, Kutas M. Offline dominance and zeugmatic similarity normings of variably ambiguous words assessed against a neural language model (BERT). Behav Res Methods, 2023, 55(4): 1537-1557.
57. Sarraju A, Ouyang D, Itchhaporia D. The opportunities and challenges of large language models in cardiology. JACC Adv, 2023, 2(7): 100438.
58. Vaid A, Duong SQ, Lampert J, et al. Local large language models for privacy-preserving accelerated review of historic echocardiogram reports. J Am Med Inform Assoc, 2024, 31(9): 2097-2102.
59. Kwon T, Ong KT, Kang D, et al. Large language models are clinical reasoners: Reasoning-aware diagnosis framework with prompt-generated rationales. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(16): 18417-18425.
60. Novak A, Rode F, Lisičić A, et al. The pulse of artificial intelligence in cardiology: A comprehensive evaluation of state-of-the-art large language models for potential use in clinical cardiology. MedRxiv, 2023. https://doi.org/10.1101/2023.08.08.23293689.
61. Quer G, Topol EJ. The potential for large language models to transform cardiovascular medicine. Lancet Digit Health, 2024, 6(10): e767-e771.

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Latest ArticlesResearch progress on deep learning in the assisted diagnosis of valvular heart disease

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