- 1. Department of Cardiology, West China Chunxi Hospital, Sichuan University and the Fourth People’s Hospital of Sichuan Province, Chengdu, Sichuan 610016, P. R. China;
- 2. Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
HUO Chang,
Email: 2488066421@qq.com
Cardiovascular disease (CVD) has caused a huge burden of disease worldwide, and accurate diagnosis and assessment of CVD has a clear significance for improving the prognosis of patients. The development of artificial intelligence (AI) and its rapid application in the medical field have enabled new approaches for the analysis and fitting of various CVD data. At present, in addition to structured medical records, the CVD field also includes a large number of non-linear data brought by imaging and electrophysiological examinations. How to use AI to process such multi-source data has been explored by a large number of studies. Therefore, this review discusses the existing ways of processing various multi-source heterogeneous data with existing artificial intelligence technologies by summarizing various existing studies, and analyzes their possible advantages and disadvantages, in order to provide a basis for the future application of AI in CVD.
Citation： HUO Chang, LI Yiming. Methods and prospects of using artificial intelligence to process multi-source data of cardiovascular disease. West China Medical Journal, 2023, 38(5): 758-764. doi: 10.7507/1002-0179.202302041 Copy
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| 2. | Mensah GA, Roth GA, Fuster V. The global burden of cardiovascular diseases and risk factors: 2020 and beyond. J Am Coll Cardiol, 2019, 74(20): 2529-2532. |
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| 15. | Mitchell TM, Mitchell TM. Machine learning, vol. 1. New York: McGraw-Hill, 1997. |
| 16. | Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med, 2001, 23(1): 89-109. |
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- 1. Lim GB. Global burden of cardiovascular disease. Nat Rev Cardiol, 2013, 10(2): 59.
- 2. Mensah GA, Roth GA, Fuster V. The global burden of cardiovascular diseases and risk factors: 2020 and beyond. J Am Coll Cardiol, 2019, 74(20): 2529-2532.
- 3. Roth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study. J Am Coll Cardiol, 2020, 76(25): 2982-3021.
- 4. 马丽媛, 王增武, 樊静, 等. 《中国心血管健康与疾病报告 2021》要点解读. 中国全科医学, 2022, 25(27): 3331-3346.
- 5. Moon H, Ahn H, Kodell RL, et al. Ensemble methods for classification of patients for personalized medicine with high-dimensional data. Artif Intell Med, 2007, 41(3): 197-207.
- 6. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
- 7. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7): 1527-1554.
- 8. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
- 9. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
- 10. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017, 19: 221-248.
- 11. Coenen A, Kim YH, Kruk M, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging, 2018, 11(6): e007217.
- 12. Martin-Isla C, Campello VM, Izquierdo C, et al. Image-based cardiac diagnosis with machine learning: a review. Front Cardiovasc Med, 2020, 7: 1.
- 13. Huang SC, Pareek A, Seyyedi S, et al. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digit Med, 2020, 3: 136.
- 14. Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. New York: John Wiley & Sons, 2021.
- 15. Mitchell TM, Mitchell TM. Machine learning, vol. 1. New York: McGraw-Hill, 1997.
- 16. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med, 2001, 23(1): 89-109.
- 17. Chen T, Guestrin C. Xgboost: a scalable tree boosting system//ACM SIGKDD. Proceedings of the 22nd ACM SIGKDD International Conference on knowledge discovery and data mining. San Francisco: ACM SIGKDD International Conference, 2016: 785-794.
- 18. Than MP, Pickering JW, Sandoval Y, et al. Machine learning to predict the likelihood of acute myocardial infarction. Circulation, 2019, 140(11): 899-909.
- 19. 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.
- 20. Hernandez-Suarez DF, Kim Y, Villablanca P, et al. Machine learning prediction models for in-hospital mortality after transcatheter aortic valve replacement. JACC Cardiovasc Interv, 2019, 12(14): 1328-1338.
- 21. Khera R, Haimovich J, Hurley NC, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol, 2021, 6(6): 633-641.
- 22. Wallert J, Tomasoni M, Madison G, et al. Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data. BMC Med Inform Decis Mak, 2017, 17(1): 99.
- 23. Lipton ZC. The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue, 2018, 16(3): 31-57.
- 24. LeCun Y, Cortes C, Christopher JC. The mnist database of handwritten digits. New York: Courant Institute, 2013.
- 25. Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database//IEEE. 2009 IEEE conference on computer vision and pattern recognition. Miami: IEEE, 2009: 248-255.
- 26. van Velzen SGM, Lessmann N, Velthuis BK, et al. Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocols. Radiology, 2020, 295(1): 66-79.
- 27. Wolterink JM, Leiner T, de Vos BD, et al. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal, 2016, 34: 123-136.
- 28. Payer C, Štern D, Bischof H, et al. Multi-label whole heart segmentation using CNNs and anatomical label configurations. International workshop on statistical atlases and computational models of the heart. Berlin: Springer, 2017: 190-198.
- 29. de Vos BD, Wolterink JM, de Jong PA, et al. ConvNet-based localization of anatomical structures in 3-D medical images. IEEE Trans Med Imaging, 2017, 36(7): 1470-1481.
- 30. Zhuang X, Li L, Payer C, et al. Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med Image Anal, 2019, 58: 101537.
- 31. Chen C, Qin C, Qiu H, et al. Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med, 2020, 7: 25.
- 32. Schaap M, Metz CT, van Walsum T, et al. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal, 2009, 13(5): 701-714.
- 33. Wolterink JM, van Hamersvelt RW, Viergever MA, et al. Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med Image Anal, 2019, 51: 46-60.
- 34. Choi AD, Marques H, Kumar V, et al. CT Evaluation by artificial intelligence for atherosclerosis, stenosis and vascular morphology (CLARIFY): a multi-center, international study. J Cardiovasc Comput Tomogr, 2021, 15(6): 470-476.
- 35. Li Y, Wu Y, He J, et al. Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography. Eur Radiol, 2022, 32(9): 6037-6045.
- 36. Lee MCH, Petersen K, Pawlowski N, et al. TeTrIS: template transformer networks for image segmentation with shape priors. IEEE Trans Med Imaging, 2019, 38(11): 2596-2606.
- 37. Itu L, Rapaka S, Passerini T, et al. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985), 2016, 121(1): 42-52.
- 38. von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, et al. Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol, 2019, 29(5): 2378-2387.
- 39. Wang ZQ, Zhou YJ, Zhao YX, et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography. J Geriatr Cardiol, 2019, 16(1): 42-48.
- 40. Bai W, Sinclair M, Tarroni G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson, 2018, 20(1): 65.
- 41. Bello GA, Dawes TJW, Duan J, et al. Deep learning cardiac motion analysis for human survival prediction. Nat Mach Intell, 2019, 1: 95-104.
- 42. Popescu DM, Shade JK, Lai C, et al. Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart. Nat Cardiovasc Res, 2022, 1(4): 334-343.
- 43. Cockrum J, Chen D, Nakashima M, et al. Deep learning analysis using cardiovascular magnetic resonance imaging for risk prediction in cardiac amyloidosis. J Am Coll Cardiol, 2022, 79(9): 1193.
- 44. Moccia S, Banali R, Martini C, et al. Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images. MAGMA, 2019, 32(2): 187-195.
- 45. Diller GP, Vahle J, Radke R, et al. Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease. BMC Med Imaging, 2020, 20(1): 113.
- 46. Martini N, Aimo A, Barison A, et al. Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance. J Cardiovasc Magn Reson, 2020, 22(1): 84.
- 47. Schuuring MJ, Išgum I, Cosyns B, et al. Routine echocardiography and artificial intelligence solutions. Front Cardiovasc Med, 2021, 8: 648877.
- 48. Narang A, Bae R, Hong H, et al. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol, 2021, 6(6): 624-632.
- 49. Medvedofsky D, Mor-Avi V, Amzulescu M, et al. Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study. Eur Heart J Cardiovasc Imaging, 2018, 19(1): 47-58.
- 50. Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature, 2020, 580(7802): 252-256.
- 51. Tromp J, Seekings PJ, Hung CL, et al. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Health, 2022, 4(1): e46-e54.
- 52. Andreassen BS, Veronesi F, Gerard O, et al. Mitral annulus segmentation using deep learning in 3-D transesophageal echocardiography. IEEE J Biomed Health Inform, 2020, 24(4): 994-1003.
- 53. Jeganathan J, Knio Z, Amador Y, et al. Artificial intelligence in mitral valve analysis. Ann Card Anaesth, 2017, 20(2): 129-134.
- 54. Ghesu FC, Krubasik E, Georgescu B, et al. Marginal space deep learning: efficient architecture for volumetric image parsing. IEEE Trans Med Imaging, 2016, 35(5): 1217-1228.
- 55. Wahlang I, Saha G, Jasiński M, et al. Deep learning methods for classification of certain abnormalities in echocardiography. Elec, 2021, 10(4): 495.
- 56. Kwon JM, Kim KH, Jeon KH, et al. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography, 2019, 36(2): 213-218.
- 57. Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med, 2020, 3: 10.
- 58. Zhang H, Mu L, Hu S, et al. Comparison of physician visual assessment with quantitative coronary angiography in assessment of stenosis severity in China. JAMA Intern Med, 2018, 178(2): 239-247.
- 59. Au B, Shaham U, Dhruva S, et al. Automated characterization of stenosis in invasive coronary angiography images with convolutional neural networks. ResearchGate, 2018: 2-13.
- 60. Meng Y, Du Z, Zhao C, et al. Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms. Lic, 2022: 1-22.
- 61. Zhao C, Vij A, Malhotra S, et al. Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms. Comput Biol Med, 2021, 136: 104667.
- 62. Nishi T, Yamashita R, Imura S, et al. Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease. Int J Cardiol, 2021, 333: 55-59.
- 63. Min HS, Ryu D, Kang SJ, et al. Prediction of coronary stent underexpansion by pre-procedural intravascular ultrasound-based deep learning. JACC Cardiovasc Interv, 2021, 14(9): 1021-1029.
- 64. Li G, Ye W, Lin L, et al. An artificial-intelligence approach to ECG analysis. IEEE Eng Med Biol Mag, 2000, 19(2): 95-100.
- 65. Siontis KC, Noseworthy PA, Attia ZI, et al. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol, 2021, 18(7): 465-478.
- 66. Ribeiro AH, Ribeiro MH, Paixão GMM, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun, 2020, 11(1): 1760.
- 67. Kashou AH, Ko WY, Attia ZI, et al. A comprehensive artificial intelligence-enabled electrocardiogram interpretation program. Cardiovasc Digit Health J, 2020, 1(2): 62-70.
- 68. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet, 2019, 394(10201): 861-867.
- 69. Tison GH, Zhang J, Delling FN, et al. Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery. Circ Cardiovasc Qual Outcomes, 2019, 12(9): e005289.
- 70. Attia ZI, DeSimone CV, Dillon JJ, et al. Novel bloodless potassium determination using a signal-processed single-lead ECG. J Am Heart Assoc, 2016, 5(1): e002746.
- 71. Raghunath S, Ulloa Cerna AE, Jing L, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med, 2020, 26(6): 886-891.
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