Research progress on artificial intelligence application in the perioperative period of cardiovascular surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

JIANG Hong , LIU Zeye ,  PAN Xiangbin

Department of Structural Heart Disease, National Center for Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100037, P. R. China;

Corresponding author：

PAN Xiangbin, Email: panxiangbin@fuwaihospital.org

Keywords：

Artificial intelligence; machine learning; deep learning; cardiovascular surgery; perioperative period; review

DOI：

10.7507/1007-4848.202404002

Video：

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With the advancement and development of computer technology, the medical decision-making system based on artificial intelligence (AI) has been widely applied in clinical practice. In the perioperative period of cardiovascular surgery, AI can be applied to preoperative diagnosis, intraoperative, and postoperative risk management. This article introduces the application and development of AI during the perioperative period of cardiovascular surgery, including preoperative auxiliary diagnosis, intraoperative risk management, postoperative management, and full process auxiliary decision-making management. At the same time, it explores the challenges and limitations of the application of AI and looks forward to the future development direction.

Citation： JIANG Hong, LIU Zeye, PAN Xiangbin. Research progress on artificial intelligence application in the perioperative period of cardiovascular surgery. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(1): 54-59. doi: 10.7507/1007-4848.202404002 Copy

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2.	Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health, 2018, 15(12): 2796.
3.	Cai A, Dan Z, Zhou Y, et al. Prognostic implications of machine learning-derived echocardiographic phenotypes in community hypertensive patients. Clin Exp Hypertens, 2023, 45(1): 2236334.
4.	Xu X, Jia Q, Yuan H, et al. A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Med Image Anal, 2023, 90: 102953.
5.	Daidone M, Ferrantelli S, Tuttolomondo A. Machine learning applications in stroke medicine: Advancements, challenges, and future prospectives. Neural Regen Res, 2024, 19(4): 769-773.
6.	Bai L, Wu Y, Li G, et al. AI-enabled organoids: Construction, analysis, and application. Bioact Mater, 2023, 31: 525-548.
7.	Liu F, Liu C, Tang X, et al. Predictive value of machine learning models in postoperative mortality of older adults patients with hip fracture: A systematic review and meta-analysis. Arch Gerontol Geriatr, 2023, 115: 105120.
8.	Kobrinskii BA. Artificial intelligence: Problems, solutions, and prospects. Pattern Recogn. Image Anal, 2023, 33(3): 217-220.
9.	Maheshwari K, Cywinski JB, Papay F, et al. Artificial intelligence for perioperative medicine: Perioperative intelligence. Anesth Analg, 2023, 136(4): 637-645.
10.	van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol, 2019, 29(5): 2350-2359.
11.	Banchhor SK, Londhe ND, Araki T, et al. Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. Comput Biol Med, 2018, 101: 184-198.
12.	Dinesh M, Devarakota P, Kumar J. Automatic detection of plaques with severe stenosis in coronary vessels of CT angiography. SPIE, 2010.
13.	Arnoldi E, Gebregziabher M, Schoepf UJ, et al. Automated computer-aided stenosis detection at coronary CT angiography: Initial experience. Eur Radiol, 2010, 20(5): 1160-1167.
14.	Cano-Espinosa C, González G, Washko GR, et al. Automated agatston score computation in non-ECG gated CT scans using deep learning. Proc SPIE Int Soc Opt Eng, 2018, 10574: 105742K.
15.	Al'Aref SJ, Maliakal G, Singh G, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: Analysis from the CONFIRM registry. Eur Heart J, 2020, 41(3): 359-367.
16.	Kang D, Dey D, Slomka PJ, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging (Bellingham), 2015, 2(1): 014003.
17.	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.
18.	Wesselink EM, Kappen TH, van Klei WA, et al. Intraoperative hypotension and delirium after on-pump cardiac surgery. Br J Anaesth, 2015, 115(3): 427-433.
19.	Wang D, Ding X, Su Y, et al. Incidence, risk factors, and outcomes of severe hypoxemia after cardiac surgery. Front Cardiovasc Med, 2022, 9: 934533.
20.	Khoury H, Lyons R, Sanaiha Y, et al. Deep venous thrombosis and pulmonary embolism in cardiac surgical patients. Ann Thorac Surg, 2020, 109(6): 1804-1810.
21.	Dunning J, Fabbri A, Kolh PH, et al. Guideline for resuscitation in cardiac arrest after cardiac surgery. Eur J Cardiothorac Surg, 2009, 36(1): 3-28.
22.	Ruetzler K, Khanna AK, Sessler DI. Myocardial injury after noncardiac surgery: Preoperative, intraoperative, and postoperative aspects, implications, and directions. Anesth Analg, 2020, 131(1): 173-186.
23.	Wesselink EM, Kappen TH, Torn HM, et al. Intraoperative hypotension and the risk of postoperative adverse outcomes: A systematic review. Br J Anaesth, 2018, 121(4): 706-721.
24.	D'Amico F, Fominskiy EV, Turi S, et al. Intraoperative hypotension and postoperative outcomes: A meta-analysis of randomised trials. Br J Anaesth, 2023, 131(5): 823-831.
25.	Müller-Wirtz LM, Ruetzler K, Rössler J. Intraoperative hypotension and delirium. J Clin Anesth, 2023, 89: 111153.
26.	Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology, 2018, 129(4): 663-674.
27.	Parsons H, Zilahi G. Pro: Hypotension prediction index—A new tool to predict hypotension in cardiac surgery? J Cardiothorac Vasc Anesth, 2023, 37(10): 2133-2136.
28.	Michard F, Futier E. Predicting intraoperative hypotension: From hope to hype and back to reality. Br J Anaesth, 2023, 131(2): 199-201.
29.	Condello I, Santarpino G, Nasso G, et al. Management algorithms and artificial intelligence systems for cardiopulmonary bypass. Perfusion, 2022, 37(8): 765-772.
30.	Jiang H, Liu L, Wang Y, et al. Machine learning for the prediction of complications in patients after mitral valve surgery. Front Cardiovasc Med, 2021, 8: 771246.
31.	Li Y, Xu J, Wang Y, et al. A novel machine learning algorithm, Bayesian networks model, to predict the high-risk patients with cardiac surgery-associated acute kidney injury. Clin Cardiol, 2020, 43(7): 752-761.
32.	Gao Y, Liu X, Wang L, et al. Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting. Front Cardiovasc Med, 2022, 9: 881881.
33.	黄琦, 关美娇, 邹彬, 等. 机器学习模型预测心脏外科手术患者术后谵妄的有效性. 临床麻醉学杂志, 2023, 39(4): 363-369.Huang Q, Guan MJ, Zou B, et al. Effectiveness of machine learning models in predicting postoperative delirium in patients undergoing cardiac surgery. J Clin Anesthesiol, 2023, 39(4): 363-369.
34.	Li T, Yang Y, Huang J, et al. Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation. BMC Cardiovasc Disord, 2022, 22(1): 288.
35.	Kim RB, Alge OP, Liu G, et al. Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system. Sci Rep, 2022, 12(1): 11347.
36.	Karri R, Kawai A, Thong YJ, et al. Machine learning outperforms existing clinical scoring tools in the prediction of postoperative atrial fibrillation during intensive care unit admission after cardiac surgery. Heart Lung Circ, 2021, 30(12): 1929-1937.
37.	Park J, Bonde PN. Machine learning in cardiac surgery: Predicting mortality and readmission. ASAIO J, 2022, 68(12): 1490-1500.
38.	Zea-Vera R, Ryan CT, Navarro SM, et al. Development of a machine learning model to predict outcomes and cost after cardiac surgery. Ann Thorac Surg, 2023, 115(6): 1533-1542.
39.	Luo L, Huang SQ, Liu C, et al. Machine learning-based risk model for predicting early mortality after surgery for infective endocarditis. J Am Heart Assoc, 2022, 11(11): e025433.
40.	Fernandes MPB, Armengol de la Hoz M, Rangasamy V, et al. Machine learning models with preoperative risk factors and intraoperative hypotension parameters predict mortality after cardiac surgery. J Cardiothorac Vasc Anesth, 2021, 35(3): 857-865.
41.	Allyn J, Allou N, Augustin P, et al. A comparison of a machine learning model with EuroSCORE Ⅱ in predicting mortality after elective cardiac surgery: A decision curve analysis. PLoS One, 2017, 12(1): e0169772.
42.	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.
43.	Kveton M, Hudec L, Vykopal I, et al. Digital pathology in cardiac transplant diagnostics: From biopsies to algorithms. Cardiovasc Pathol, 2024, 68: 107587.
44.	Kampaktsis PN, Siouras A, Doulamis IP, et al. Machine learning-based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis. Clin Transplant, 2023, 37(1): e14845.
45.	Zhou Y, Chen S, Rao Z, et al. Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China. Int J Cardiol, 2021, 339: 21-27.
46.	Agasthi P, Buras MR, Smith SD, et al. Machine learning helps predict long-term mortality and graft failure in patients undergoing heart transplant. Gen Thorac Cardiovasc Surg, 2020, 68(12): 1369-1376.
47.	Shear TD, Deshur M, Benson J, et al. The effect of an electronic dynamic cognitive aid versus a static cognitive aid on the management of a simulated crisis: A randomized controlled trial. J Med Syst, 2018, 43(1): 6.
48.	Hasimbegovic E, Papp L, Grahovac M, et al. A sneak-peek into the physician's Brain: A retrospective machine learning-driven investigation of decision-making in TAVR versus SAVR for young high-risk patients with severe symptomatic aortic stenosis. J Pers Med, 2021, 11(11): 1062.
49.	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.
50.	Cook CM, Warisawa T, Howard JP, et al. Algorithmic versus expert human interpretation of instantaneous wave-free ratio coronary pressure-wire pull back data. JACC Cardiovasc Interv, 2019, 12(14): 1315-1324.
51.	Khalsa RK, Khashkhusha A, Zaidi S, et al. Artificial intelligence and cardiac surgery during COVID-19 era. J Card Surg, 2021, 36(5): 1729-1733.

1. Jiang D. Application of artificial intelligence in computer network technology in big data era. 2021 International Conference on Big Data Analysis and Computer Science (BDACS), Kunming, China, 2021: 254-257.
2. Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health, 2018, 15(12): 2796.
3. Cai A, Dan Z, Zhou Y, et al. Prognostic implications of machine learning-derived echocardiographic phenotypes in community hypertensive patients. Clin Exp Hypertens, 2023, 45(1): 2236334.
4. Xu X, Jia Q, Yuan H, et al. A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Med Image Anal, 2023, 90: 102953.
5. Daidone M, Ferrantelli S, Tuttolomondo A. Machine learning applications in stroke medicine: Advancements, challenges, and future prospectives. Neural Regen Res, 2024, 19(4): 769-773.
6. Bai L, Wu Y, Li G, et al. AI-enabled organoids: Construction, analysis, and application. Bioact Mater, 2023, 31: 525-548.
7. Liu F, Liu C, Tang X, et al. Predictive value of machine learning models in postoperative mortality of older adults patients with hip fracture: A systematic review and meta-analysis. Arch Gerontol Geriatr, 2023, 115: 105120.
8. Kobrinskii BA. Artificial intelligence: Problems, solutions, and prospects. Pattern Recogn. Image Anal, 2023, 33(3): 217-220.
9. Maheshwari K, Cywinski JB, Papay F, et al. Artificial intelligence for perioperative medicine: Perioperative intelligence. Anesth Analg, 2023, 136(4): 637-645.
10. van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol, 2019, 29(5): 2350-2359.
11. Banchhor SK, Londhe ND, Araki T, et al. Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. Comput Biol Med, 2018, 101: 184-198.
12. Dinesh M, Devarakota P, Kumar J. Automatic detection of plaques with severe stenosis in coronary vessels of CT angiography. SPIE, 2010.
13. Arnoldi E, Gebregziabher M, Schoepf UJ, et al. Automated computer-aided stenosis detection at coronary CT angiography: Initial experience. Eur Radiol, 2010, 20(5): 1160-1167.
14. Cano-Espinosa C, González G, Washko GR, et al. Automated agatston score computation in non-ECG gated CT scans using deep learning. Proc SPIE Int Soc Opt Eng, 2018, 10574: 105742K.
15. Al'Aref SJ, Maliakal G, Singh G, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: Analysis from the CONFIRM registry. Eur Heart J, 2020, 41(3): 359-367.
16. Kang D, Dey D, Slomka PJ, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging (Bellingham), 2015, 2(1): 014003.
17. 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.
18. Wesselink EM, Kappen TH, van Klei WA, et al. Intraoperative hypotension and delirium after on-pump cardiac surgery. Br J Anaesth, 2015, 115(3): 427-433.
19. Wang D, Ding X, Su Y, et al. Incidence, risk factors, and outcomes of severe hypoxemia after cardiac surgery. Front Cardiovasc Med, 2022, 9: 934533.
20. Khoury H, Lyons R, Sanaiha Y, et al. Deep venous thrombosis and pulmonary embolism in cardiac surgical patients. Ann Thorac Surg, 2020, 109(6): 1804-1810.
21. Dunning J, Fabbri A, Kolh PH, et al. Guideline for resuscitation in cardiac arrest after cardiac surgery. Eur J Cardiothorac Surg, 2009, 36(1): 3-28.
22. Ruetzler K, Khanna AK, Sessler DI. Myocardial injury after noncardiac surgery: Preoperative, intraoperative, and postoperative aspects, implications, and directions. Anesth Analg, 2020, 131(1): 173-186.
23. Wesselink EM, Kappen TH, Torn HM, et al. Intraoperative hypotension and the risk of postoperative adverse outcomes: A systematic review. Br J Anaesth, 2018, 121(4): 706-721.
24. D'Amico F, Fominskiy EV, Turi S, et al. Intraoperative hypotension and postoperative outcomes: A meta-analysis of randomised trials. Br J Anaesth, 2023, 131(5): 823-831.
25. Müller-Wirtz LM, Ruetzler K, Rössler J. Intraoperative hypotension and delirium. J Clin Anesth, 2023, 89: 111153.
26. Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology, 2018, 129(4): 663-674.
27. Parsons H, Zilahi G. Pro: Hypotension prediction index—A new tool to predict hypotension in cardiac surgery? J Cardiothorac Vasc Anesth, 2023, 37(10): 2133-2136.
28. Michard F, Futier E. Predicting intraoperative hypotension: From hope to hype and back to reality. Br J Anaesth, 2023, 131(2): 199-201.
29. Condello I, Santarpino G, Nasso G, et al. Management algorithms and artificial intelligence systems for cardiopulmonary bypass. Perfusion, 2022, 37(8): 765-772.
30. Jiang H, Liu L, Wang Y, et al. Machine learning for the prediction of complications in patients after mitral valve surgery. Front Cardiovasc Med, 2021, 8: 771246.
31. Li Y, Xu J, Wang Y, et al. A novel machine learning algorithm, Bayesian networks model, to predict the high-risk patients with cardiac surgery-associated acute kidney injury. Clin Cardiol, 2020, 43(7): 752-761.
32. Gao Y, Liu X, Wang L, et al. Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting. Front Cardiovasc Med, 2022, 9: 881881.
33. 黄琦, 关美娇, 邹彬, 等. 机器学习模型预测心脏外科手术患者术后谵妄的有效性. 临床麻醉学杂志, 2023, 39(4): 363-369.Huang Q, Guan MJ, Zou B, et al. Effectiveness of machine learning models in predicting postoperative delirium in patients undergoing cardiac surgery. J Clin Anesthesiol, 2023, 39(4): 363-369.
34. Li T, Yang Y, Huang J, et al. Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation. BMC Cardiovasc Disord, 2022, 22(1): 288.
35. Kim RB, Alge OP, Liu G, et al. Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system. Sci Rep, 2022, 12(1): 11347.
36. Karri R, Kawai A, Thong YJ, et al. Machine learning outperforms existing clinical scoring tools in the prediction of postoperative atrial fibrillation during intensive care unit admission after cardiac surgery. Heart Lung Circ, 2021, 30(12): 1929-1937.
37. Park J, Bonde PN. Machine learning in cardiac surgery: Predicting mortality and readmission. ASAIO J, 2022, 68(12): 1490-1500.
38. Zea-Vera R, Ryan CT, Navarro SM, et al. Development of a machine learning model to predict outcomes and cost after cardiac surgery. Ann Thorac Surg, 2023, 115(6): 1533-1542.
39. Luo L, Huang SQ, Liu C, et al. Machine learning-based risk model for predicting early mortality after surgery for infective endocarditis. J Am Heart Assoc, 2022, 11(11): e025433.
40. Fernandes MPB, Armengol de la Hoz M, Rangasamy V, et al. Machine learning models with preoperative risk factors and intraoperative hypotension parameters predict mortality after cardiac surgery. J Cardiothorac Vasc Anesth, 2021, 35(3): 857-865.
41. Allyn J, Allou N, Augustin P, et al. A comparison of a machine learning model with EuroSCORE Ⅱ in predicting mortality after elective cardiac surgery: A decision curve analysis. PLoS One, 2017, 12(1): e0169772.
42. 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.
43. Kveton M, Hudec L, Vykopal I, et al. Digital pathology in cardiac transplant diagnostics: From biopsies to algorithms. Cardiovasc Pathol, 2024, 68: 107587.
44. Kampaktsis PN, Siouras A, Doulamis IP, et al. Machine learning-based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis. Clin Transplant, 2023, 37(1): e14845.
45. Zhou Y, Chen S, Rao Z, et al. Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China. Int J Cardiol, 2021, 339: 21-27.
46. Agasthi P, Buras MR, Smith SD, et al. Machine learning helps predict long-term mortality and graft failure in patients undergoing heart transplant. Gen Thorac Cardiovasc Surg, 2020, 68(12): 1369-1376.
47. Shear TD, Deshur M, Benson J, et al. The effect of an electronic dynamic cognitive aid versus a static cognitive aid on the management of a simulated crisis: A randomized controlled trial. J Med Syst, 2018, 43(1): 6.
48. Hasimbegovic E, Papp L, Grahovac M, et al. A sneak-peek into the physician's Brain: A retrospective machine learning-driven investigation of decision-making in TAVR versus SAVR for young high-risk patients with severe symptomatic aortic stenosis. J Pers Med, 2021, 11(11): 1062.
49. 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.
50. Cook CM, Warisawa T, Howard JP, et al. Algorithmic versus expert human interpretation of instantaneous wave-free ratio coronary pressure-wire pull back data. JACC Cardiovasc Interv, 2019, 12(14): 1315-1324.
51. Khalsa RK, Khashkhusha A, Zaidi S, et al. Artificial intelligence and cardiac surgery during COVID-19 era. J Card Surg, 2021, 36(5): 1729-1733.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress on artificial intelligence application in the perioperative period of cardiovascular surgery

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