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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
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
DOI: 10.26442/20751753.2023.1.202095
DOI: 10.26442/20751753.2023.1.202095
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
________________________________________________
DOI: 10.26442/20751753.2023.1.202095
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Аннотация
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
Keywords: cardiooncology, artificial intelligence, machine learning
Ключевые слова: кардиоонкология, искусственный интеллект, машинное обучение
________________________________________________
Keywords: cardiooncology, artificial intelligence, machine learning
Полный текст
Список литературы
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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
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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
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
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
________________________________________________
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
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Авторы
Ю.И. Бузиашвили, Э.У. Асымбекова, Э.Ф. Тугеева, Ф.Р. Акилджонов*
ФГБУ «Национальный медицинский исследовательский центр сердечно-сосудистой хирургии им. А.Н. Бакулева» Минздрава России, Москва, Россия
*firdavs96_tths@mail.ru
Bakulev National Medical Research Center of Cardiovascular Surgery, Moscow, Russia
*firdavs96_tths@mail.ru
ФГБУ «Национальный медицинский исследовательский центр сердечно-сосудистой хирургии им. А.Н. Бакулева» Минздрава России, Москва, Россия
*firdavs96_tths@mail.ru
________________________________________________
Bakulev National Medical Research Center of Cardiovascular Surgery, Moscow, Russia
*firdavs96_tths@mail.ru
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