Kardiyolojide Yapay Zekanın Yeri
Özet
Yapay zeka, kardiyolojide görüntü elde etme, tanı koyma ve tedavi süreçlerini optimize ederek maliyetleri düşüren ve insan gücünden tasarruf sağlayan devrim niteliğinde çözümler sunmaktadır. Koroner arter kalsiyum skorlaması, plak analizi ve kalp boşluklarının otomatik segmentasyonu gibi alanlarda yüksek doğruluk oranlarına ulaşan algoritmalar, klinisyenlere karar verme aşamalarında akıllı bir yardımcı olarak hizmet etmektedir. Ayrıca akıllı saatler ve giyilebilir teknolojiler aracılığıyla atriyal fibrilasyon ve kalp yetmezliği gibi durumların erken teşhisi mümkün hale gelmiş, girişimsel kardiyolojide ise robotik sistemler işlem sürelerini kısaltmaya başlamıştır. Gelecekte yapay zekanın, hızı ve kaliteyi artırarak iş akışlarını iyileştirmesi, ancak her hastanın kişisel ve sosyal durumunu değerlendiren uzman klinisyenlerin yanında tamamlayıcı bir araç olarak kalması öngörülmektedir.
Artificial intelligence offers revolutionary solutions in cardiology by optimizing image acquisition, diagnosis, and treatment processes, thereby reducing costs and saving manpower. Reaching high accuracy rates in areas such as coronary artery calcium scoring, plaque analysis, and automated segmentation of heart chambers, algorithms serve as intelligent assistants for clinicians during decision-making. Furthermore, early diagnosis of conditions like atrial fibrillation and heart failure has become possible through smartwatches and wearable technologies, while robotic systems in interventional cardiology have begun to shorten procedure times. In the future, AI is expected to improve workflows by increasing speed and quality, yet it is envisioned to remain a complementary tool alongside expert clinicians who evaluate each patient's personal and social circumstances.
Referanslar
Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach.
Darcy AM, Louie AK, Roberts LW. Machine Learning and the Profession of Medicine. JAMA. 2016 Feb 9;315(6):551-2. doi: 10.1001/jama.2015.18421. PMID: 26864406.
Dawes, T. J., de Marvao, A., Shi, W., Fletcher, T., Watson, G. M., Wharton, J., ... & O’Regan, D. P. (2017). Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology, 283(2), 381-390.
Yeboah, J., McClelland, R. L., Polonsky, T. S., Burke, G. L., Sibley, C. T., O’Leary, D., ... & Herrington, D. M. (2012). Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. Jama, 308(8), 788-795.
Wolterink, J. M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., & Išgum, I. (2016). Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Medical image analysis, 34, 123-136.
Lessmann, N., Išgum, I., Setio, A. A., de Vos, B. D., Ciompi, F., de Jong, P. A., ... & van Ginneken, B. (2016, March). Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT. In Medical Imaging 2016: Computer-Aided Diagnosis (Vol. 9785, p. 978511). International Society for Optics and Photonics
Feuchtner, G., Kerber, J., Burghard, P., Dichtl, W., Friedrich, G., Bonaros, N., & Plank, F. (2017). The high-risk criteria low-attenuation plaque< 60 HU and the napkin-ring sign are the most powerful predictors of MACE: a long-term follow-up study. European Heart Journal-Cardiovascular Imaging, 18(7), 772-779.
Conte, E., Annoni, A., Pontone, G., Mushtaq, S., Guglielmo, M., Baggiano, A., ... & Andreini, D. (2017). Evaluation of coronary plaque characteristics with coronary computed tomography angiography in patients with non-obstructive coronary artery disease: a long-term follow-up study. European Heart Journal-Cardiovascular Imaging, 18(10), 1170-1178.
Zreik, M., Van Hamersvelt, R. W., Wolterink, J. M., Leiner, T., Viergever, M. A., & Išgum, I. (2018). A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE transactions on medical imaging, 38(7), 1588-1598.
Toth, G., Hamilos, M., Pyxaras, S., Mangiacapra, F., Nelis, O., De Vroey, F., ... & De Bruyne, B. (2014). Evolving concepts of angiogram: fractional flow reserve discordances in 4000 coronary stenoses. European heart journal, 35(40), 2831-2838.
Nagel, E., Greenwood, J. P., McCann, G. P., Bettencourt, N., Shah, A. M., Hussain, S. T., ... & Berry, C. (2019). Magnetic resonance perfusion or fractional flow reserve in coronary disease. New England Journal of Medicine, 380(25), 2418-2428
Taylor, C. A., Fonte, T. A., & Min, J. K. (2013). Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. Journal of the American College of Cardiology, 61(22), 2233-2241
Itu, L., Rapaka, S., Passerini, T., Georgescu, B., Schwemmer, C., Schoebinger, M., ... & Comaniciu, D. (2016). A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. Journal of applied physiology, 121(1), 42-52.
Coenen, A., Kim, Y. H., Kruk, M., Tesche, C., De Geer, J., Kurata, A., ... & Nieman, K. (2018). Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography–based fractional flow reserve: result from the MACHINE consortium. Circulation: Cardiovascular Imaging, 11(6), e007217.
Dey, D., Gaur, S., Ovrehus, K. A., Slomka, P. J., Betancur, J., Goeller, M., ... & Norgaard, B. L. (2018). Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. European radiology, 28(6), 2655-2664
Tao, Q., Yan, W., Wang, Y., Paiman, E. H., Shamonin, D. P., Garg, P., ... & van der Geest, R. J. (2019). Deep learning–based method for fully automatic quantification of left ventricle function from cine MR images: a multivendor, multicenter study. Radiology, 290(1), 81-88.
Ruijsink, B., Puyol-Antón, E., Oksuz, I., Sinclair, M., Bai, W., Schnabel, J. A., ... & King, A. P. (2020). Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function. Cardiovascular Imaging, 13(3), 684-695.
Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., ... & Deo, R. C. (2018). Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation, 138(16), 1623-1635
Moghaddasi H, Nourian S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput Biol Med. 2016 Jun 1;73:47-55. doi: 10.1016/j.compbiomed.2016.03.026. Epub 2016 Apr 2. PMID: 27082766
Tabassian, M., Alessandrini, M., Herbots, L., Mirea, O., Pagourelias, E. D., Jasaityte, R., ... & D’hooge, J. (2017). Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification. The international journal of cardiovascular imaging, 33(8), 1159-1167
.( Narula, S., Shameer, K., Salem Omar, A. M., Dudley, J. T., & Sengupta, P. P. (2016). Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. Journal of the American College of Cardiology, 68(21), 2287-2295.
Sengupta, P. P., Huang, Y. M., Bansal, M., Ashrafi, A., Fisher, M., Shameer, K., ... & Dudley, J. T. (2016). Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circulation: Cardiovascular Imaging, 9(6), e004330
Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370
Ten Eyck, L., MacLeod, S., Hawkins, K., Guimont, R., & Hartley, S. (2019). The impact of a heart failure management program in a Medicare advantage population. Population health management, 22(2), 153-161.
Conraads, V. M., Spruit, M. A., Braunschweig, F., Cowie, M. R., Tavazzi, L., Borggrefe, M., ... & van Veldhuisen, D. J. (2014). Physical activity measured with implanted devices predicts patient outcome in chronic heart failure. Circulation: Heart Failure, 7(2), 279-287.
Corder, K., Brage, S., & Ekelund, U. (2007). Accelerometers and pedometers: methodology and clinical application. Current Opinion in Clinical Nutrition & Metabolic Care, 10(5), 597-603
Lindholm, D., & Holzmann, M. (2018). Machine learning for improved detection of myocardial infarction in patients presenting with chest pain in the emergency department. Journal of the American College of Cardiology, 71(11S), A225-A225.
Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet?. Heart, 104(14), 1156-1164.
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664.)
Sardar, P., Abbott, J. D., Kundu, A., Aronow, H. D., Granada, J. F., & Giri, J. (2019). Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance. Cardiovascular Interventions, 12(14), 1293-1303
Shah, A. P., & Rubin, S. A. (2007). Errors in the computerized electrocardiogram interpretation of cardiac rhythm. Journal of electrocardiology, 40(5), 385-390.
.( Poon, K., Okin, P. M., & Kligfield, P. (2005). Diagnostic performance of a computer-based ECG rhythm algorithm. Journal of electrocardiology, 38(3), 235-238
Tison GH, Sanchez JM, Ballinger B, Singh A, Olgin JE, Pletcher MJ, Vittinghoff E, Lee ES, Fan SM, Gladstone RA, Mikell C, Sohoni N, Hsieh J, Marcus GM. Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch. JAMA Cardiol. 2018 May 1;3(5):409-416. doi: 10.1001/jamacardio.2018.0136. PMID: 29562087; PMCID: PMC5875390.
Bumgarner, J. M., Lambert, C. T., Hussein, A. A., Cantillon, D. J., Baranowski, B., Wolski, K., ... & Tarakji, K. G. (2018). Smartwatch algorithm for automated detection of atrial fibrillation. Journal of the American College of Cardiology, 71(21), 2381-2388.
Shcherbina, A., Mattsson, C. M., Waggott, D., Salisbury, H., Christle, J. W., Hastie, T., ... & Ashley, E. A. (2017). Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort. Journal of personalized medicine, 7(2), 3
Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, Balasubramanian V, Russo AM, Rajmane A, Cheung L, Hung G, Lee J, Kowey P, Talati N, Nag D, Gummidipundi SE, Beatty A, Hills MT, Desai S, Granger CB, Desai M, Turakhia MP; Apple Heart Study Investigators. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019 Nov 14;381(20):1909-1917. doi: 10.1056/NEJMoa1901183. PMID: 31722151; PMCID: PMC8112605.
Guo Y, Wang H, Zhang H, Liu T, Liang Z, Xia Y, Yan L, Xing Y, Shi H, Li S, Liu Y, Liu F, Feng M, Chen Y, Lip GYH; MAFA II Investigators. Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. J Am Coll Cardiol. 2019 Nov 12;74(19):2365-2375. doi: 10.1016/j.jacc.2019.08.019. Epub 2019 Sep 2. PMID: 31487545
Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiol. 2019 May 1;4(5):428-436. doi: 10.1001/jamacardio.2019.0640. PMID: 30942845; PMCID: PMC6537816.)
Kolossváry, M., Kellermayer, M., Merkely, B., & Maurovich-Horvat, P. (2018). Cardiac computed tomography radiomics. Journal of thoracic imaging, 33(1), 26-34
( Kotu, L. P., Engan, K., Skretting, K., Måløy, F., Ørn, S., Woie, L., & Eftestøl, T. (2013). Probability mapping of scarred myocardium using texture and intensity features in CMR images. Biomedical engineering online, 12(1), 1-19.
Larroza, A., Materka, A., López-Lereu, M. P., Monmeneu, J. V., Bodí, V., & Moratal, D. (2017). Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. European journal of radiology, 92, 78-83