Abstract
Coronary artery disease is one of its most important causes of early mortality in western world. Therefore, clinicians seek to improve diagnostic procedures in order to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics is often performed in a sequential manner, where the four diagnostic steps typically consist of evaluation of (1) signs and symptoms of the disease and electrocardiogram (ECG) at rest, (2) sequential ECG testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography, that is considered as the “gold standard” reference method. Our study focuses on improving diagnostic and probabilistic interpretation of scintigraphic images obtained from the penultimate step. We use automatic image parameterization on multiple resolutions, based on spatial association rules. Extracted image parameters are combined into more informative composite parameters by means of principle component analysis, and finally used to build automatic classifiers with neural networks and naive Bayes learning methods. Experiments show that our approach significantly increases diagnostic accuracy, specificity and sensitivity with respect to clinical results.
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References
Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321–355 (1988)
Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing. Foundations, vol. 1. MIT Press, Cambridge (1986)
Diamond, G.A., Forester, J.S.: Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. New England Journal of Medicine 300(1350) (1979)
General Electric. Ectoolbox protocol operator’s guide (2001)
Gamberger, D., Lavrac, N., Krstacic, G.: Active subgroup mining: a case study in coronary heart disease risk group detection. Artif. Intell. Med. 28(1), 27–57 (2003)
Garcia, E.V., Cooke, C.D., Folks, R.D., Santana, C.A., Krawczynska, E.G., De Braal, L., Ezquerra, N.F.: Diagnostic performance of an expert system for the interpretation of myocardial perfusion spect studies. J. Nucl. Med. 42(8), 1185–1191 (2001)
Kukar, M., Kononenko, I.: Cost-sensitive learning with neural networks. In: Proc. European Conference on Artificial Intelligence ECAI 1998, Brighton, UK, pp. 445–449 (1998)
Kukar, M., Kononenko, I., Grošelj, C., Kralj, K., Fettich, J.: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine 16(1), 25–50 (1999)
Kukar, M., Šajn, L., Grošelj, C., Grošelj, J.: Multi-resolution image parametrization in stepwise diagnostics of coronary artery disease. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 119–129. Springer, Heidelberg (2007)
Kurgan, L.A., Cios, K.J., Tadeusiewicz, R.: Knowledge discovery approach to automated cardiac spect diagnosis. Artif. Intell. Med. 23(2), 149–169 (2001)
Lindahl, D., Palmer, J., Pettersson, J., White, T., Lundin, A., Edenbrandt, L.: Scintigraphic diagnosis of coronary artery disease: myocardial bull’s-eye images contain the important information. Clinical Physiology 6(18) (1998)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Nixon, M., Aguado, A.S.: Feature Extraction and Image Processing, 2nd edn. Academic Press, Elsevier (2008)
Ohlsson, M.: WeAidU–a decision support system for myocardial perfusion images using artificial neural networks. Artificial Intelligence in Medicine 30, 49–60 (2004)
Olona-Cabases, M.: The probability of a correct diagnosis. In: Candell-Riera, J., Ortega-Alcalde, D. (eds.) Nuclear Cardiology in Everyday Practice, pp. 348–357. Kluwer, Dordrecht (1994)
Slomka, P.J., Nishina, H., Berman, D.S., Akincioglu, C., Abidov, A., Friedman, J.D., Hayes, S.W., Germano, G.: Automated quantification of myocardial perfusion spect using simplified normal limits. J. Nucl. Cardiol. 12(1), 66–77 (2005)
Šajn, L., Kononenko, I.: Multiresolution image parametrization for improving texture classification. EURASIP J. Adv. Signal Process 2008(1), 1–12 (2008)
Štrumbelj, E., Kononenko, I.: An efficient explanation of individual classifications using game theory. Journal of Machine Learning Research 11, 1–18 (2010)
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Kukar, M., Grošelj, C. (2011). Supporting Diagnostics of Coronary Artery Disease with Neural Networks. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_9
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DOI: https://doi.org/10.1007/978-3-642-20282-7_9
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