Application of artificial intelligence in prevention and treatment of cardiovascular diseases_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIU Zeye ^1,2,3 ,  PAN Xiangbin ^1,2,3

1. Department of Structural Heart Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100037, P. R. China;
2. National Health Commission Key Laboratory of Cardiovascular Regeneration Medicine, Beijing, 100037, P. R. China;
3. National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, 100037, P. R. China;

Corresponding author：

PAN Xiangbin, Email: panxiangbin@fuwaihospital.org

Keywords：

Cardiovascular diseases; artificial intelligence; computer vision; natural language processing; robot; machine learning; review

DOI：

10.7507/1007-4848.202201014

Video：

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With the development of science and technology, artificial intelligence is gradually integrated into every aspect of daily life and the medical field is no exception. Cardiovascular diseases, as the first killer to global health, is the focus of new technologies and methods. In this study, the application of computer vision, natural language processing, robotics and machine learning in cardiovascular disease studies were reviewed and prospected, in order to promote the development for new technologies and applications in the future.

Citation： LIU Zeye, PAN Xiangbin. Application of artificial intelligence in prevention and treatment of cardiovascular diseases. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(9): 1230-1235. doi: 10.7507/1007-4848.202201014 Copy

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2.	Lu H, Li Y, Chen M, et al. Brain intelligence: Go beyond artificial intelligence. Mobile Netw Appl, 2018, 23(2): 368-375.
3.	陈发达. 网络思政对社会焦点的捕捉和解读. 中学政治教学参考, 2021, 269(43): 69-70.
4.	Okwuosa IS, Lewsey SC, Adesiyun T, et al. Worldwide disparities in cardiovascular disease: Challenges and solutions. Int J Cardiol, 2016, 202: 433-440.
5.	Shen C, Ge J. Epidemic of cardiovascular disease in China: Current perspective and prospects for the future. Circulation, 2018, 138(4): 342-344.
6.	Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine. J Family Med Prim Care, 2019, 8(7): 2328-2331.
7.	Atlam HF, Walters RJ, Wills GB. Intelligence of things: Opportunities & challenges. Paris: 2018 3rd Cloudification of the Internet of Things (CIoT), 2018.
8.	Patel KK, Patel SM. Internet of things-IOT: Definition, characteristics, architecture, enabling technologies, application & future challenges. Int J Eng Sci Comp, 2016, 6(5): 6122-6131.
9.	Zheng NN. The new era of artificial intelligence. Chin J Int Sci Tech, 2019, 1(1): 1-3.
10.	崔雍浩, 商聪, 陈锶奇, 等. 人工智能综述: AI的发展. 无线电通信技术, 2019, 45(3): 225-231.
11.	龙慧, 朱定局, 田娟. 深度学习在智能机器人中的应用研究综述. 计算机科学, 2018, 45(S2): 43-47,52.
12.	Szeliski R. Computer Vision: Algorithms and Applications. Germany: Springer Science & Business Media, 2010.
13.	Barszczyk A, Lee K. Measuring blood pressure: From cuff to smartphone. Curr Hypertens Rep, 2019, 21(11): 84.
14.	Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J, 2020, 41(46): 4400-4411.
15.	Toba S, Mitani Y, Yodoya N, et al. Prediction of pulmonary to systemic flow ratio in patients with congenital heart disease using deep learning—Based analysis of chest radiographs. JAMA Cardiol, 2020, 5(4): 449-457.
16.	Hwang EJ, Park S, Jin KN, et al. Development and validation of a deep learning—Based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open, 2019, 2(3): e191095.
17.	Mori H, Inai K, Sugiyama H, et al. Diagnosing atrial septal defect from electrocardiogram with deep learning. Pediatric cardiology, 2021, 42(6): 1379-1387.
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19.	Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med, 2019, 25(1): 65-69.
20.	Zeleznik R, Foldyna B, Eslami P, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun, 2021, 12(1): 715.
21.	Wu L, Dong B, Liu X, et al. Standard echocardiographic view recognition in diagnosis of congenital heart defects in children using deep learning based on knowledge distillation. Front Pediatr, 2022, 9: 770182.
22.	Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med, 2021, 27(5): 882-891.
23.	Lo Muzio FP, Rozzi G, Rossi S, et al. Artificial intelligence supports decision making during open-chest surgery of rare congenital heart defects. J Clin Med, 2021, 10(22): 5330.
24.	Falk T, Mai D, Bensch R, et al. U-Net: Deep learning for cell counting, detection, and morphometry. Nat Methods, 2019, 16(1): 67-70.
25.	Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
26.	Shi Y, Gao L, Wang J, et al. Development of automatic diagnosis and evaluation system for ECG recorder. Zhongguo Yi Liao Qi Xie Za Zhi, 2017, 41(2): 117-119.
27.	Simon ST, Mandair D, Tiwari P, et al. Prediction of drug-induced long QT syndrome using machine learning applied to harmonized electronic health record data. J Cardiovasc Pharmacol Ther, 2021, 26(4): 335-340.
28.	Montalt-Tordera J, Quail M, Steeden JA, et al. Reducing contrast agent dose in cardiovascular MR angiography with deep learning. J Magn Reson Imaging, 2021, 54(3): 795-805.
29.	Li X, Chen H, Qi X, et al. H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging, 2018, 37(12): 2663-2674.
30.	Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(12): 2481-2495.
31.	Chen LC, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV). 2018. 801-818.
32.	Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: An introduction. J Am Med Inform Assoc, 2011, 18(5): 544-551.
33.	Bian Y, Xiang Y, Tong B, et al. Artificial intelligence-assisted system in postoperative follow-up of orthopedic patients: Exploratory quantitative and qualitative study. J Med Internet Res, 2020, 22(5): e16896.
34.	Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw, 2020, 130: 22-32.
35.	Liu J, Wang H, Yang Z, et al. Deep learning-based computer-aided heart sound analysis in children with left-to-right shunt congenital heart disease. Int J Cardiol, 2022, 348: 58-64.
36.	Xu W, Yu K, Xu J, et al. Artificial intelligence technology in cardiac auscultation screening for congenital heart disease: Present and future. Zhejiang Da Xue Xue Bao Yi Xue Ban, 2020, 49(5): 548-555.
37.	Irvin J, Rajpurkar P, Ko M, et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence. 2019. 590-597.
38.	Gal D, Thijs B, Glänzel W, et al. Hot topics and trends in cardiovascular research. Eur Heart J, 2019, 40(28): 2363-2374.
39.	Gates AJ, Ke Q, Varol O, et al. Nature's reach: Narrow work has broad impact. Nature, 2019, 575(7781): 32-34.
40.	王薪宇. 基于深度学习和云机器人的工业机器人未来发展方向的研究. 科技创新导报, 2016, 13(10): 7-9,11.
41.	Tamadon I, Mamone V, Huan Y, et al. ValveTech: A novel robotic approach for minimally invasive aortic valve replacement. IEEE Trans Biomed Eng, 2021, 68(4): 1238-1249.
42.	Ou-Yang WB, Li SJ, Wang SZ, et al. Echocardiographic guided closure of perimembranous ventricular septal defects. Ann Thorac Surg, 2015, 100(4): 1398-1402.
43.	Deng RD, Zhang FW, Zhao GZ, et al. A novel double-balloon catheter for percutaneous balloon pulmonary valvuloplasty under echocardiographic guidance only. J Cardiol, 2020, 76(3): 236-243.
44.	Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255-260.
45.	邢宇彤, 刘建成, 孙百臣, 等. 区域医疗中心人工智能辅助诊断肺结节的临床应用. 中国胸心血管外科临床杂志, 2021, 28(10): 1178-1182.
46.	D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): A modelling study of pooled datasets. Lancet, 2021, 397(10270): 199-207.
47.	Chen Q, Zhang B, Yang J, et al. Predicting intensive care unit length of stay after acute type A aortic dissection surgery using machine learning. Front Cardiovasc Med, 2021, 8: 675431.
48.	Begic E, Gurbeta Pokvic L, Begic Z, et al. From heart murmur to echocardiography—Congenital heart defects diagnostics using machine-learning algorithms. Psychiatr Danub, 2021, 33(Suppl 13): 236-246.
49.	Chang Junior J, Binuesa F, Caneo LF, et al. Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study. PLoS One, 2020, 15(9): e0238199.
50.	潘湘斌. 3D打印技术在心脏领域的应用前景和挑战. 中华医学杂志, 2017, 97(16): 1201-1203.
51.	黄树杰, 龙安妮, 王迁, 等. 基于3D打印技术的个性化主动脉覆膜支架的动物实验研究. 中国循环杂志, 2020, 35(4): 390-394.
52.	邱旭, 吕滨, 徐楠, 等. 应用3D打印技术及超声引导介入技术治疗多发房间隔缺损的可行性. 中华医学杂志, 2017, 97(16): 1214-1217.
53.	潘湘斌, 欧阳文斌, 李琦. 经外科途径先天性心脏病介入治疗技术质量控制和进展报告. 中国循环杂志, 2020, 35(10): 955-959.
54.	Gao S, He L, Chen Y, et al. Public perception of artificial intelligence in medical care: Content analysis of social media. J Med Internet Res, 2020, 22(7): e16649.
55.	姜杰, 耿国军, 朱晓雷, 等. 人工智能(AI)在胸外科中的应用. 中国胸心血管外科临床杂志, 2021, 28(10): 1156-1159.

1. Turing AM, Haugeland J. Computing Machinery and Intelligence. Cambridge, MA: MIT Press, 1950.
2. Lu H, Li Y, Chen M, et al. Brain intelligence: Go beyond artificial intelligence. Mobile Netw Appl, 2018, 23(2): 368-375.
3. 陈发达. 网络思政对社会焦点的捕捉和解读. 中学政治教学参考, 2021, 269(43): 69-70.
4. Okwuosa IS, Lewsey SC, Adesiyun T, et al. Worldwide disparities in cardiovascular disease: Challenges and solutions. Int J Cardiol, 2016, 202: 433-440.
5. Shen C, Ge J. Epidemic of cardiovascular disease in China: Current perspective and prospects for the future. Circulation, 2018, 138(4): 342-344.
6. Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine. J Family Med Prim Care, 2019, 8(7): 2328-2331.
7. Atlam HF, Walters RJ, Wills GB. Intelligence of things: Opportunities & challenges. Paris: 2018 3rd Cloudification of the Internet of Things (CIoT), 2018.
8. Patel KK, Patel SM. Internet of things-IOT: Definition, characteristics, architecture, enabling technologies, application & future challenges. Int J Eng Sci Comp, 2016, 6(5): 6122-6131.
9. Zheng NN. The new era of artificial intelligence. Chin J Int Sci Tech, 2019, 1(1): 1-3.
10. 崔雍浩, 商聪, 陈锶奇, 等. 人工智能综述: AI的发展. 无线电通信技术, 2019, 45(3): 225-231.
11. 龙慧, 朱定局, 田娟. 深度学习在智能机器人中的应用研究综述. 计算机科学, 2018, 45(S2): 43-47,52.
12. Szeliski R. Computer Vision: Algorithms and Applications. Germany: Springer Science & Business Media, 2010.
13. Barszczyk A, Lee K. Measuring blood pressure: From cuff to smartphone. Curr Hypertens Rep, 2019, 21(11): 84.
14. Lin S, Li Z, Fu B, et al. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J, 2020, 41(46): 4400-4411.
15. Toba S, Mitani Y, Yodoya N, et al. Prediction of pulmonary to systemic flow ratio in patients with congenital heart disease using deep learning—Based analysis of chest radiographs. JAMA Cardiol, 2020, 5(4): 449-457.
16. Hwang EJ, Park S, Jin KN, et al. Development and validation of a deep learning—Based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open, 2019, 2(3): e191095.
17. Mori H, Inai K, Sugiyama H, et al. Diagnosing atrial septal defect from electrocardiogram with deep learning. Pediatric cardiology, 2021, 42(6): 1379-1387.
18. Giudicessi JR, Schram M, Bos JM, et al. Artificial intelligence—Enabled assessment of the heart rate corrected QT interval using a mobile electrocardiogram device. Circulation, 2021, 143(13): 1274-1286.
19. Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med, 2019, 25(1): 65-69.
20. Zeleznik R, Foldyna B, Eslami P, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun, 2021, 12(1): 715.
21. Wu L, Dong B, Liu X, et al. Standard echocardiographic view recognition in diagnosis of congenital heart defects in children using deep learning based on knowledge distillation. Front Pediatr, 2022, 9: 770182.
22. Arnaout R, Curran L, Zhao Y, et al. An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease. Nat Med, 2021, 27(5): 882-891.
23. Lo Muzio FP, Rozzi G, Rossi S, et al. Artificial intelligence supports decision making during open-chest surgery of rare congenital heart defects. J Clin Med, 2021, 10(22): 5330.
24. Falk T, Mai D, Bensch R, et al. U-Net: Deep learning for cell counting, detection, and morphometry. Nat Methods, 2019, 16(1): 67-70.
25. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
26. Shi Y, Gao L, Wang J, et al. Development of automatic diagnosis and evaluation system for ECG recorder. Zhongguo Yi Liao Qi Xie Za Zhi, 2017, 41(2): 117-119.
27. Simon ST, Mandair D, Tiwari P, et al. Prediction of drug-induced long QT syndrome using machine learning applied to harmonized electronic health record data. J Cardiovasc Pharmacol Ther, 2021, 26(4): 335-340.
28. Montalt-Tordera J, Quail M, Steeden JA, et al. Reducing contrast agent dose in cardiovascular MR angiography with deep learning. J Magn Reson Imaging, 2021, 54(3): 795-805.
29. Li X, Chen H, Qi X, et al. H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging, 2018, 37(12): 2663-2674.
30. Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(12): 2481-2495.
31. Chen LC, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV). 2018. 801-818.
32. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: An introduction. J Am Med Inform Assoc, 2011, 18(5): 544-551.
33. Bian Y, Xiang Y, Tong B, et al. Artificial intelligence-assisted system in postoperative follow-up of orthopedic patients: Exploratory quantitative and qualitative study. J Med Internet Res, 2020, 22(5): e16896.
34. Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw, 2020, 130: 22-32.
35. Liu J, Wang H, Yang Z, et al. Deep learning-based computer-aided heart sound analysis in children with left-to-right shunt congenital heart disease. Int J Cardiol, 2022, 348: 58-64.
36. Xu W, Yu K, Xu J, et al. Artificial intelligence technology in cardiac auscultation screening for congenital heart disease: Present and future. Zhejiang Da Xue Xue Bao Yi Xue Ban, 2020, 49(5): 548-555.
37. Irvin J, Rajpurkar P, Ko M, et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence. 2019. 590-597.
38. Gal D, Thijs B, Glänzel W, et al. Hot topics and trends in cardiovascular research. Eur Heart J, 2019, 40(28): 2363-2374.
39. Gates AJ, Ke Q, Varol O, et al. Nature's reach: Narrow work has broad impact. Nature, 2019, 575(7781): 32-34.
40. 王薪宇. 基于深度学习和云机器人的工业机器人未来发展方向的研究. 科技创新导报, 2016, 13(10): 7-9,11.
41. Tamadon I, Mamone V, Huan Y, et al. ValveTech: A novel robotic approach for minimally invasive aortic valve replacement. IEEE Trans Biomed Eng, 2021, 68(4): 1238-1249.
42. Ou-Yang WB, Li SJ, Wang SZ, et al. Echocardiographic guided closure of perimembranous ventricular septal defects. Ann Thorac Surg, 2015, 100(4): 1398-1402.
43. Deng RD, Zhang FW, Zhao GZ, et al. A novel double-balloon catheter for percutaneous balloon pulmonary valvuloplasty under echocardiographic guidance only. J Cardiol, 2020, 76(3): 236-243.
44. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255-260.
45. 邢宇彤, 刘建成, 孙百臣, 等. 区域医疗中心人工智能辅助诊断肺结节的临床应用. 中国胸心血管外科临床杂志, 2021, 28(10): 1178-1182.
46. D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): A modelling study of pooled datasets. Lancet, 2021, 397(10270): 199-207.
47. Chen Q, Zhang B, Yang J, et al. Predicting intensive care unit length of stay after acute type A aortic dissection surgery using machine learning. Front Cardiovasc Med, 2021, 8: 675431.
48. Begic E, Gurbeta Pokvic L, Begic Z, et al. From heart murmur to echocardiography—Congenital heart defects diagnostics using machine-learning algorithms. Psychiatr Danub, 2021, 33(Suppl 13): 236-246.
49. Chang Junior J, Binuesa F, Caneo LF, et al. Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study. PLoS One, 2020, 15(9): e0238199.
50. 潘湘斌. 3D打印技术在心脏领域的应用前景和挑战. 中华医学杂志, 2017, 97(16): 1201-1203.
51. 黄树杰, 龙安妮, 王迁, 等. 基于3D打印技术的个性化主动脉覆膜支架的动物实验研究. 中国循环杂志, 2020, 35(4): 390-394.
52. 邱旭, 吕滨, 徐楠, 等. 应用3D打印技术及超声引导介入技术治疗多发房间隔缺损的可行性. 中华医学杂志, 2017, 97(16): 1214-1217.
53. 潘湘斌, 欧阳文斌, 李琦. 经外科途径先天性心脏病介入治疗技术质量控制和进展报告. 中国循环杂志, 2020, 35(10): 955-959.
54. Gao S, He L, Chen Y, et al. Public perception of artificial intelligence in medical care: Content analysis of social media. J Med Internet Res, 2020, 22(7): e16649.
55. 姜杰, 耿国军, 朱晓雷, 等. 人工智能(AI)在胸外科中的应用. 中国胸心血管外科临床杂志, 2021, 28(10): 1156-1159.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Application of artificial intelligence in prevention and treatment of cardiovascular diseases

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