Inteligencia artificial en el diagnóstico imagenológico de las alteraciones cardíacas estructurales: Una revisión sistemática.
Palabras clave:
Inteligencia artificial, Enfermedades cardíacas, Técnicas de imagenología cardíaca, Aprendizaje automatizado, Aprendizaje profundo, Cardiología, Enfermedades cardíacas estructuralesResumen
Introducción: La IA se ha implementado cada vez más en las ciencias biomédicas, con una relevante aplicación en cardiología. Esta revisión tuvo como objetivo determinar si el uso de la IA en técnicas imagenológicas en cardiología mejora el diagnóstico y manejo terapéutico de alteraciones cardíacas estructurales no congénitas. Materiales y métodos: La búsqueda se realizó en las bases de datos Pubmed, Scopus y Embase, mediante el uso de las palabras claves y términos MeSH: “Artificial Intelligence”, “Heart Diseases”, “Structural heart diseases”, “Heart Valve Diseases”, “Cardiac Imaging Techniques” y “Machine learning”, que fueron combinados con los conectores booleanos AND y OR. Se incluyeron estudios publicados entre 2004-2024 en idiomas inglés o español, realizados en humanos, sin límite de edad y/o sexo. Resultados: En total fueron seleccionados 2,407 artículos, de los cuales fueron escogidos para la revisión sistemática 22, de estos 2 fueron cohortes, 10 ensayos clínicos, 10 estudios transversales. De los modelos de IA, 10 fueron de DL, 10 de ML y 2 no fueron especificados. El rango de edad de los participantes fue de 30-70 años, con mayoría del sexo masculino. Un total de 15 artículos reportaron sensibilidad y especificidad, 7 usaron otras métricas de eficacia y precisión. Conclusiones: El apoyo de la IA en técnicas imagenológicas ha logrado una mejora en la detección de alteraciones cardíacas estructurales no congénitas. Sin embargo, los estudios revisados demuestran una aplicación limitada y una eficacia que no alcanza las expectativas previstas.
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1. Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004; 86(5):334-8. DOI: 10.1308/147870804290
2. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018; 18(8):500-10. DOI: 10.1038/s41568-018-0016-5
3. Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021; 41(6):1105-15. DOI: 10.1007/s11596-021-2474-3
4. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol. 2018; 71(23):2668-79. DOI: 10.1016/j.jacc.2018.03.521
5. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021; 25(3):1315-60. DOI: 10.1007/s11030-021-10217-3
6. Hussain S, Mubeen I, Ullah N, Shah SSUD, Khan BA, Zahoor M, et al. Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review. Biomed Res Int. 2022; 2022:5164970. DOI: 10.1155/2022/5164970
7. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017; 69(21):2657-64. DOI: 10.1016/j.jacc.2017.03.571
8. Iannattone PA, Zhao X, VanHouten J, Garg A, Huynh T. Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches. Can J Cardiol. 2020; 36(4):577-83. DOI: 10.1016/j.cjca.2019.09.013
9. Lin A, van Diemen PA, Motwani M, McElhinney P, Otaki Y, Han D, et al. Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow. Circ Cardiovasc Imaging. 2022; 15(10):e014369. DOI: 10.1161/CIRCIMAGING.122.014369
10. Singh S, Chaudhary R, Bliden KP, Tantry US, Gurbel PA, Visweswaran S, et al. Meta-Analysis of the Performance of AI-Driven ECG Interpretation in the Diagnosis of Valvular Heart Diseases. Am J Cardiol. 2024; 213:126-31. DOI: 10.1016/j.amjcard.2023.12.015
11. Gruwez H, Barthels M, Haemers P, Verbrugge FH, Dhont S, Meekers E, et al. Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm: External Validation of the AI Approach. JACC Clin Electrophysiol. 2023; 9(8 Pt 3):1771-82. DOI: 10.1016/j.jacep.2023.04.008
12. Barker J, Li X, Khavandi S, Koeckerling D, Mavilakandy A, Pepper C, et al. Machine learning in sudden cardiac death risk prediction: a systematic review. Europace. 2022; 24(11):1777-87. DOI: 10.1093/europace/euac135
13. Cikes M, Sanchez-Martinez S, Claggett B, Duchateau N, Piella G, Butakoff C, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. Eur J Heart Fail. 2019; 21(1):74-85. DOI: 10.1002/ejhf.1333
14. Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, et al. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Med Inform. 2019; 7(3):e10010. DOI: 10.2196/10010
15. Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020; 368:m689. DOI: 10.1136/bmj.m689
16. Friedrich S, Groß S, König IR, Engelhardt S, Bahls M, Heinz J, et al. Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations. Eur Heart J Digit Health. 2021; 2(3):424-36. DOI: 10.1093/ehjdh/ztab054
17. Krajcer Z. Artificial Intelligence in Cardiovascular Medicine: Historical Overview, Current Status, and Future Directions. Tex Heart Inst J. 2022; 49(2). DOI: 10.14503/THIJ-20-7527
18. Kolk MZH, Deb B, Ruipérez-Campillo S, Bhatia NK, Clopton P, Wilde AAM, et al. Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies. EBioMedicine. 2023 Mar;89:104462. DOI: 10.1016/j.ebiom.2023.104462
19. Manetas-Stavrakakis N, Sotiropoulou IM, Paraskevas T, Maneta Stavrakaki S, Bampatsias D, Xanthopoulos A, et al. Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis. J Clin Med Res. 2023; 12(20). DOI: 10.3390/jcm12206576
20. Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017; 121(9):1092-101. DOI: 10.1161/CIRCRESAHA.117.311312
21. Popat A, Yadav S, Patel SK, Baddevolu S, Adusumilli S, Rao Dasari N, et al. Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis. Cureus. 2023; 15(12):e50395. DOI: 10.7759/cureus.50395
22. Nazar W, Szymanowicz S, Nazar K, Kaufmann D, Wabich E, Braun-Dullaeus R, et al. Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review. Heart Fail Rev. 2024; 29(1):133-50. DOI: 10.1007/s10741-023-10357-8
23. Palmieri V, Montisci A, Vietri MT, Colombo PC, Sala S, Maiello C, et al. Artificial intelligence, big data and heart transplantation: Actualities. Int J Med Inform. 2023; 176:105110. DOI: 10.1016/j.ijmedinf.2023.105110
24. Talaei Khoei T, Ould Slimane H, Kaabouch N. Deep learning: systematic review, models, challenges, and research directions. Neural Comput Appl. 2023, DOI: 10.1007/s00521-023-08957-4
25. Egger J, Gsaxner C, Pepe A, Pomykala KL, Jonske F, Kurz M, et al. Medical deep learning-A systematic meta-review. Comput Methods Programs Biomed. 2022; 221:106874. DOI: 10.1016/j.cmpb.2022.106874
26. Umar U, Nayab S, Irfan R, Khan MA, Umer A. E-Cardiac care: A comprehensive systematic literature review. Sensors [Internet]. 2022; 22(20). DOI: 10.3390/s22208073
27. Panjiyar BK, Davydov G, Nashat H, Ghali S, Afifi S, Suryadevara V, et al. A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches? Cureus. 2023; 15(8):e43003. DOI: 10.7759/cureus.43003
28. Cheng S, Han Y, Jiang L, Lan Z, Guo J. National, regional, and global cardiomyopathy burden from 1990 to 2019. Front Cardiovasc Med. 2022; 9:1042448. DOI: 10.1186/s12916-023-02925-4
29. World Health Organization. World health statistics 2023: monitoring health for the SDGs, Sustainable Development Goals. World Health Organization; 2023. 130 p.
30. Ahsan MM, Siddique Z. Machine learning-based heart disease diagnosis: A systematic literature review. Artif Intell Med. 2022; 128:102289. DOI: 10.1016/j.artmed.2022.102289
31. De Cecco CN, van Assen M, Leiner T, editors. Artificial intelligence in cardiothoracic imaging. 1st ed. Cham, Switzerland: Springer Nature; 2022. 595 p. (Contemporary Medical Imaging).
32. Davies RH, Augusto JB, Bhuva A, Xue H, Treibel TA, Ye Y, et al. Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning. J Cardiovasc Magn Reson. 2022; 24(1):16. DOI: 10.1186/s12968-022-00846-4
33. Cau R, Pisu F, Pintus A, Palmisano V, Montisci R, Suri JS, et al. Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach. Eur Radiol. 2024. DOI: 10.1007/s00330-024-10640-8
34. Salehi M, Maiter A, Strickland S, Aldabbagh Z, Karunasaagarar K, Thomas R, et al. Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR. Front Cardiovasc Med. 2024; 11:1279298. DOI: 10.3389/fcvm.2024.1279298
35. Corianò M, Lanera C, De Michieli L, Perazzolo Marra M, Iliceto S, Gregori D, et al. Deep learning-based prediction of major arrhythmic events in dilated cardiomyopathy: A proof of concept study. PLoS One. 2024; 19(2):e0297793. DOI: 10.1371/journal.pone.0297793
36. Togo R, Hirata K, Manabe O, Ohira H, Tsujino I, Magota K, et al. Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps. Comput Biol Med. 2019; 104:81-6. DOI: 10.1016/j.compbiomed.2018.11.008
37. Lin X, Yang F, Chen Y, Chen X, Wang W, Chen X, et al. Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction. Front Cardiovasc Med. 2022; 9:903660. DOI: 10.3389/fcvm.2022.903660
38. Barbieri A, Imberti JF, Bartolomei M, Bonini N, Laus V, Torlai Triglia L, et al. Quantification of Myocardial Contraction Fraction with Three-Dimensional Automated, Machine-Learning-Based Left-Heart-Chamber Metrics: Diagnostic Utility in Hypertrophic Phenotypes and Normal Ejection Fraction. J Clin Med Res. 2023; 12(17). DOI: 0.3390/jcm12175525
39. Tabassian M, Sunderji I, Erdei T, Sanchez-Martinez S, Degiovanni A, Marino P, 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
40. Spielvogel CP, Haberl D, Mascherbauer K, Ning J, Kluge K, Traub-Weidinger T, et al. Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study. Lancet Digit Health. 2024; 6(4):e251-60. DOI: 10.1016/S2589-7500(23)00265-0
41. Chandra V, Sarkar PG, Singh V. Mitral valve leaflet tracking in echocardiography using custom Yolo3. Procedia Comput Sci. 2020; 171:820-8.
42. Li K, Huang Z, Cheng YC, Lee CH. A maximal figure-of-merit learning approach to maximizing mean average precision with deep neural network based classifiers. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) [Internet]. IEEE; 2014. Disponible en: http://ieeexplore.ieee.org/document/6854454/
43. Lang RM, Addetia K, Miyoshi T, Kebed K, Blitz A, Schreckenberg M, et al. Use of Machine Learning to Improve Echocardiographic Image Interpretation Workflow: A Disruptive Paradigm Change? J Am Soc Echocardiogr. 2021; 34(4):443-5. DOI: 10.1016/j.echo.2020.11.017
44. Upton R, Mumith A, Beqiri A, Parker A, Hawkes W, Gao S, et al. Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence. JACC Cardiovasc Imaging. 2022; 15(5):715-27.
45. Guo Y, Xia C, Zhong Y, Wei Y, Zhu H, Ma J, et al. Machine learning-enhanced echocardiography for screening coronary artery disease. Biomed Eng Online. 2023; 22(1):44. DOI: 10.1186/s12938-023-01106-x
46. Kuo HC, Chen SH, Chen YH, Lin YC, Chang CY, Wu YC, et al. Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone. Front Cardiovasc Med. 2022; 9:1000374.
47. Laumer F, Di Vece D, Cammann VL, Würdinger M, Petkova V, Schönberger M, et al. Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction. JAMA Cardiol. 2022; 7(5):494-503. DOI: 10.1001/jamacardio.2022.0183
48. Woodward G, Bajre M, Bhattacharyya S, Breen M, Chiocchia V, Dawes H, et al. PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography. Am Heart J. 2023; 263:123-32. DOI: 10.1016/j.ahj.2023.05.003
49. Stuckey TD, Gammon RS, Goswami R, Depta JP, Steuter JA, Meine FJ 3rd, et al. Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning. PLoS One. 2018; 13(8):e0198603. DOI: 10.1371/journal.pone.0198603
50. Dey D, Gaur S, Ovrehus KA, Slomka PJ, Betancur J, Goeller M, et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol. 2018; 28(6):2655-64. DOI: 10.1007/s00330-017-5223-z
51. Tang CX, Wang YN, Zhou F, Schoepf UJ, van Assen M, Stroud RE, et al. Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: A multi-center study and meta-analysis. Eur J Radiol. 2019; 116:90-7. DOI: 10.1016/j.ejrad.2019.04.011
52. Lipkin I, Telluri A, Kim Y, Sidahmed A, Krepp JM, Choi BG, et al. Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard. AJR Am J Roentgenol. 2022; 219(3):407-19. DOI: 10.2214/AJR.21.27289
53. Li Y, Yu M, Dai X, Lu Z, Shen C, Wang Y, et al. Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve. Radiology. 2019; 293(2):305-14. DOI: 10.1148/radiol.2019190098
54. Cho H, Kang SJ, Min HS, Lee JG, Kim WJ, Kang SH, et al. Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease. Atherosclerosis. 2021; 324:69-75. DOI: 10.1148/radiol.2019190098
55. D’Ancona G, Massussi M, Savardi M, Signoroni A, Di Bacco L, Farina D, et al. Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD. Int J Cardiol. 2023; 370:435-41. DOI: 10.1016/j.ijcard.2022.10.154
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Derechos de autor 2025 Valentina Botia-Arciniegas, Laura Margarita Calvo-Saavedra
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