Роль искусственного интеллекта в кардиоонкологии: настоящее и будущее
Роль искусственного интеллекта в кардиоонкологии: настоящее и будущее
Бузиашвили Ю.И., Асымбекова Э.У., Тугеева Э.Ф., Акилджонов Ф.Р. Роль искусственного интеллекта в кардиоонкологии: настоящее и будущее. Consilium Medicum. 2023;25(1):29–33.
DOI: 10.26442/20751753.2023.1.202095
Buziashvili YuI, Asymbekova EU, Tugeeva EF, Akildzhonov FR. The role of artificial intelligence in cardio-oncology: present and future: A review. Consilium Medicum. 2023;25(1):29–33.
DOI: 10.26442/20751753.2023.1.202095
Роль искусственного интеллекта в кардиоонкологии: настоящее и будущее
Бузиашвили Ю.И., Асымбекова Э.У., Тугеева Э.Ф., Акилджонов Ф.Р. Роль искусственного интеллекта в кардиоонкологии: настоящее и будущее. Consilium Medicum. 2023;25(1):29–33.
DOI: 10.26442/20751753.2023.1.202095
Buziashvili YuI, Asymbekova EU, Tugeeva EF, Akildzhonov FR. The role of artificial intelligence in cardio-oncology: present and future: A review. Consilium Medicum. 2023;25(1):29–33.
DOI: 10.26442/20751753.2023.1.202095
Кардиоонкология разработана как относительно новое направление в медицине, которое фокусируется на профилактике и лечении неблагоприятных сердечно-сосудистых осложнений, связанных с терапией онкологических заболеваний. Новые, более эффективные методы получения данных, такие как использование искусственного интеллекта, желательны для помощи в оценке сердечно-сосудистых событий у пациентов с онкологическими заболеваниями. Извлекая скрытые закономерности и доказательства из больших объемов медицинских данных, искусственный интеллект может создавать новые предикторы и параметры для прогнозирования рисков у пациентов с кардиоонкологическими заболеваниями.
Ключевые слова: кардиоонкология, искусственный интеллект, машинное обучение
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Cardiooncology has been developed as a relatively new branch of medicine that focuses on the prevention and treatment of adverse cardiovascular events associated with cancer therapy. Newer, more efficient data acquisition methods, such as the use of artificial intelligence, are desirable to help assess CVR in cancer patients. By extracting hidden patterns and evidence from large volumes of medical data, artificial intelligence can create new predictors and parameters to predict risks in patients with cardio-oncological diseases.
1. Sturgeon К, Deng L, Bluethmann S, et al. A population-based study of cardiovascular disease mortality in US cancer patients. Eur Heart J. 2019;40(48):3889-97. DOI:10.1093/eurheartj/ehz766
2. Herrmann J. Fr om trends to transformation: wh ere cardio-oncology is to make a difference. Eur Heart J. 2019;40(48):3898-900. DOI:10.1093/eurheartj/ehz781
3. Curigliano G, Lenihan D, Fradley M, et al. Management of cardiac disease in cancer patients throughout oncological treatment: ESMO consensus recommendations. Ann Oncol. 2020;31(2):171-90. DOI:10.1016/j.annonc.2019.10.023
4. Müller O, Baldus C. Treatment recommendations in cardio-oncology: where are we? Internist (Berl). 2020;61(11):1125-31. DOI:10.1007/s00108-020-00886-x
5. Iliescu C, Grines C, Herrmann J, et al. SCAI Expert consensus statement: Evaluation, management, and special considerations of cardio-oncology patients in the cardiac catheterization laboratory (endorsed by the cardiological society of India, and Sociedad Latino Americana de Cardiologıa intervencionista). Catheter Cardiovasc Interv. 2016;87(5):E202-23. DOI:10.1002/ccd.26379
6. Madan N, Lucas J, Akhter N, et al. Artificial intelligence and imaging: Opportunities in cardio-oncology. Am Heart J Plus. 2022;15:100126. DOI:10.1016/j.ahjo.2022.100126
7. Dey D, Slomka P, Leeson P, et al. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019;73(11):1317-35. DOI:10.1016/j.jacc.2018.12.054
8. Sarker I. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci. 2021;2(3):160. DOI:10.1007/s42979-021-00592-x
9. Martinez D, Noseworthy P, Akbilgic O, et al. Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography. Am Heart J Plus. 2022;15:100129. DOI:10.1016/j.ahjo.2022.100129
10. Currie G, Hawk K, Rohren E, et al. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiat Sci. 2019;50(4):477-87. DOI:10.1016/j.jmir.2019.09.005
11. Zhou Y, Hou Y, Hussain M, et al. Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients. J Am Heart Assoc. 2020;9(23):e019628. DOI:10.1161/JAHA.120.019628
12. Cai C, Guo P, Zhou Y, et al. Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity. J Chem Inf Model. 2019;59(3):1073-84. DOI:10.1021/acs.jcim.8b00769
13. Khurshid S, Friedman S, Reeder C, et al. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2022;145(2):122-33. DOI:10.1161/CIRCULATIONAHA.121.057480
14. Baek Y, Lee S, Choi W, Kim D. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm. Sci Rep. 2021;11(1):12818. DOI:10.1038/s41598-021-92172-5
15. Yao X, Rushlow D, Inselman J, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27(5):815-9. DOI:10.1038/s41591-021-01335-4
16. Al Hinai G, Jammoul S, Vajihi Z, Afilalo J. Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review. Eur Heart J Digit Health. 2021;2(3):416-23. DOI:10.1093/ehjdh/ztab048
17. Attia Z, Kapa S, Yao X, et al. Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. J Cardiovasc Electrophysiol. 2019;30(5):668-74. DOI:10.1111/jce.13889
18. Adedinsewo D, Carter R, Attia Z, et al. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circ Arrhythm Electrophysiol. 2020;13(8):e008437. DOI:10.1161/CIRCEP.120.008437
19. Salem J, Yang T, Moslehi J, et al. Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes. Circulation. 2019;140(13):1070-80. DOI:10.1161/CIRCULATIONAHA.119.040162
20. Ghesu F, Georgescu B, Zheng Y, et al. Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT scans. IEEE Trans Pattern Anal Mach Intell. 2019;41(1):176-89. DOI:10.1109/TPAMI.2017.2782687
21. Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020;3:10. DOI:10.1038/s41746-019-0216-8
22. Plana J, Galderisi M, Barac A, et al. Expert consensus for multimodality imaging evaluation of adult patients during and after cancer therapy: a report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging. 2014;15(10):1063-93. DOI:10.1093/ehjci/jeu192
23. Oikonomou E, Kokkinidis D, Kampaktsis P, et al. Assessment of Prognostic Value of Left Ventricular Global Longitudinal Strain for Early Prediction of Chemotherapy-Induced Cardiotoxicity: A Systematic Review and Meta-analysis. JAMA Cardiol. 2019;4(10):1007-18. DOI:10.1001/jamacardio.2019.2952
24. Tabassian M, Sunderji I, Erdei T, et al. Diagnosis of Heart Failure with Preserved Ejection Fraction: Machine Learning of Spatiotemporal Variations in Left Ventricular Deformation. J Am Soc Echocardiogr. 2018;31(12):1272-84.e9. DOI:10.1016/j.echo.2018.07.013
25. Ruddy K, Sangaralingham L, Van Houten H, et al. Utilization of Cardiac Surveillance Tests in Survivors of Breast Cancer and Lymphoma After Anthracycline-Based Chemotherapy. Circ Cardiovasc Qual Outcomes. 2020;13(3):e005984. DOI:10.1161/CIRCOUTCOMES.119.005984
26. Zhang J, Gajjala S, Agrawal P, et al. Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation. 2018;138(16):1623-35. DOI:10.1161/CIRCULATIONAHA.118.034338
27. Knackstedt C, Bekkers S, Schummers G, et al. Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs Multicenter Study. J Am Coll Cardiol. 2015;66(13):1456-66. DOI:10.1016/j.jacc.2015.07.052
28. Dobson R, Ghosh A, Ky B, et al. BSE and BCOS Guideline for Transthoracic Echocardiographic Assessment of Adult Cancer Patients Receiving Anthracyclines and/or Trastuzumab. JACC CardioOncol. 2021;3(1):1-16. DOI:10.1016/j.jaccao.2021.01.011
29. Deng Y, Cai P, Zhang L, et al. Myocardial strain analysis of echocardiography based on deep learning. Front Cardiovasc Med. 2022;9:1067760. DOI:10.3389/fcvm.2022.1067760
30. Farsalinos K, Daraban A, Ünlü S, et al. Head-to-Head Comparison of Global Longitudinal Strain Measurements among Nine Different Vendors: The EACVI/ASE Inter-Vendor Comparison Study. J Am Soc Echocardiogr. 2015;28(10):1171-e2. DOI:10.1016/j.echo.2015.06.011
31. Luo X, Gan W, Wang L, et al. A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks. Comput Intell Neurosci. 2021;2021:8829639. DOI:10.1155/2021/8829639
32. Salte I, Østvik A, Smistad E, et al. Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. JACC Cardiovasc Imaging. 2021;14(10):1918-28. DOI:10.1016/j.jcmg.2021.04.018
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1. Sturgeon К, Deng L, Bluethmann S, et al. A population-based study of cardiovascular disease mortality in US cancer patients. Eur Heart J. 2019;40(48):3889-97. DOI:10.1093/eurheartj/ehz766
2. Herrmann J. Fr om trends to transformation: wh ere cardio-oncology is to make a difference. Eur Heart J. 2019;40(48):3898-900. DOI:10.1093/eurheartj/ehz781
3. Curigliano G, Lenihan D, Fradley M, et al. Management of cardiac disease in cancer patients throughout oncological treatment: ESMO consensus recommendations. Ann Oncol. 2020;31(2):171-90. DOI:10.1016/j.annonc.2019.10.023
4. Müller O, Baldus C. Treatment recommendations in cardio-oncology: where are we? Internist (Berl). 2020;61(11):1125-31. DOI:10.1007/s00108-020-00886-x
5. Iliescu C, Grines C, Herrmann J, et al. SCAI Expert consensus statement: Evaluation, management, and special considerations of cardio-oncology patients in the cardiac catheterization laboratory (endorsed by the cardiological society of India, and Sociedad Latino Americana de Cardiologıa intervencionista). Catheter Cardiovasc Interv. 2016;87(5):E202-23. DOI:10.1002/ccd.26379
6. Madan N, Lucas J, Akhter N, et al. Artificial intelligence and imaging: Opportunities in cardio-oncology. Am Heart J Plus. 2022;15:100126. DOI:10.1016/j.ahjo.2022.100126
7. Dey D, Slomka P, Leeson P, et al. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019;73(11):1317-35. DOI:10.1016/j.jacc.2018.12.054
8. Sarker I. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci. 2021;2(3):160. DOI:10.1007/s42979-021-00592-x
9. Martinez D, Noseworthy P, Akbilgic O, et al. Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography. Am Heart J Plus. 2022;15:100129. DOI:10.1016/j.ahjo.2022.100129
10. Currie G, Hawk K, Rohren E, et al. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiat Sci. 2019;50(4):477-87. DOI:10.1016/j.jmir.2019.09.005
11. Zhou Y, Hou Y, Hussain M, et al. Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients. J Am Heart Assoc. 2020;9(23):e019628. DOI:10.1161/JAHA.120.019628
12. Cai C, Guo P, Zhou Y, et al. Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity. J Chem Inf Model. 2019;59(3):1073-84. DOI:10.1021/acs.jcim.8b00769
13. Khurshid S, Friedman S, Reeder C, et al. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2022;145(2):122-33. DOI:10.1161/CIRCULATIONAHA.121.057480
14. Baek Y, Lee S, Choi W, Kim D. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm. Sci Rep. 2021;11(1):12818. DOI:10.1038/s41598-021-92172-5
15. Yao X, Rushlow D, Inselman J, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27(5):815-9. DOI:10.1038/s41591-021-01335-4
16. Al Hinai G, Jammoul S, Vajihi Z, Afilalo J. Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review. Eur Heart J Digit Health. 2021;2(3):416-23. DOI:10.1093/ehjdh/ztab048
17. Attia Z, Kapa S, Yao X, et al. Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. J Cardiovasc Electrophysiol. 2019;30(5):668-74. DOI:10.1111/jce.13889
18. Adedinsewo D, Carter R, Attia Z, et al. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circ Arrhythm Electrophysiol. 2020;13(8):e008437. DOI:10.1161/CIRCEP.120.008437
19. Salem J, Yang T, Moslehi J, et al. Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes. Circulation. 2019;140(13):1070-80. DOI:10.1161/CIRCULATIONAHA.119.040162
20. Ghesu F, Georgescu B, Zheng Y, et al. Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT scans. IEEE Trans Pattern Anal Mach Intell. 2019;41(1):176-89. DOI:10.1109/TPAMI.2017.2782687
21. Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020;3:10. DOI:10.1038/s41746-019-0216-8
22. Plana J, Galderisi M, Barac A, et al. Expert consensus for multimodality imaging evaluation of adult patients during and after cancer therapy: a report from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging. 2014;15(10):1063-93. DOI:10.1093/ehjci/jeu192
23. Oikonomou E, Kokkinidis D, Kampaktsis P, et al. Assessment of Prognostic Value of Left Ventricular Global Longitudinal Strain for Early Prediction of Chemotherapy-Induced Cardiotoxicity: A Systematic Review and Meta-analysis. JAMA Cardiol. 2019;4(10):1007-18. DOI:10.1001/jamacardio.2019.2952
24. Tabassian M, Sunderji I, Erdei T, et al. Diagnosis of Heart Failure with Preserved Ejection Fraction: Machine Learning of Spatiotemporal Variations in Left Ventricular Deformation. J Am Soc Echocardiogr. 2018;31(12):1272-84.e9. DOI:10.1016/j.echo.2018.07.013
25. Ruddy K, Sangaralingham L, Van Houten H, et al. Utilization of Cardiac Surveillance Tests in Survivors of Breast Cancer and Lymphoma After Anthracycline-Based Chemotherapy. Circ Cardiovasc Qual Outcomes. 2020;13(3):e005984. DOI:10.1161/CIRCOUTCOMES.119.005984
26. Zhang J, Gajjala S, Agrawal P, et al. Fully Automated Echocardiogram Interpretation in Clinical Practice. Circulation. 2018;138(16):1623-35. DOI:10.1161/CIRCULATIONAHA.118.034338
27. Knackstedt C, Bekkers S, Schummers G, et al. Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs Multicenter Study. J Am Coll Cardiol. 2015;66(13):1456-66. DOI:10.1016/j.jacc.2015.07.052
28. Dobson R, Ghosh A, Ky B, et al. BSE and BCOS Guideline for Transthoracic Echocardiographic Assessment of Adult Cancer Patients Receiving Anthracyclines and/or Trastuzumab. JACC CardioOncol. 2021;3(1):1-16. DOI:10.1016/j.jaccao.2021.01.011
29. Deng Y, Cai P, Zhang L, et al. Myocardial strain analysis of echocardiography based on deep learning. Front Cardiovasc Med. 2022;9:1067760. DOI:10.3389/fcvm.2022.1067760
30. Farsalinos K, Daraban A, Ünlü S, et al. Head-to-Head Comparison of Global Longitudinal Strain Measurements among Nine Different Vendors: The EACVI/ASE Inter-Vendor Comparison Study. J Am Soc Echocardiogr. 2015;28(10):1171-e2. DOI:10.1016/j.echo.2015.06.011
31. Luo X, Gan W, Wang L, et al. A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks. Comput Intell Neurosci. 2021;2021:8829639. DOI:10.1155/2021/8829639
32. Salte I, Østvik A, Smistad E, et al. Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography. JACC Cardiovasc Imaging. 2021;14(10):1918-28. DOI:10.1016/j.jcmg.2021.04.018
ФГБУ «Национальный медицинский исследовательский центр сердечно-сосудистой хирургии им. А.Н. Бакулева» Минздрава России, Москва, Россия
*firdavs96_tths@mail.ru
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Yuri I. Buziashvili, Elmira U. Asymbekova, Elvina F. Tugeeva, Firdavsdzhon R. Akildzhonov*
Bakulev National Medical Research Center of Cardiovascular Surgery, Moscow, Russia
*firdavs96_tths@mail.ru