Abstract
Coronary artery disease is one of the world’s most important causes of early mortality, so any improvements of diagnostic procedures are highly appreciated. In the clinical setting, coronary artery disease diagnostics is typically performed in a sequential manner. The four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and ECG (electrocardiogram) at rest, (2) ECG testing during a controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography (which is considered as the “gold standard” reference method). In our study we focus on improving diagnostic performance of the third diagnostic level (myocardial perfusion scintigraphy). This diagnostic level consists of series of medical images that are easily obtained and the imaging procedure represents only a minor threat to patients’ health. In clinical practice, these images are manually described (parameterized) and subsequently evaluated by expert physicians. In our paper we present an innovative alternative to manual image evaluation – an automatic image parametrization on multiple resolutions, based on texture description with specialized association rules, and image evaluation with machine learning methods. Our results show that multi-resolution image parameterizations equals the physicians in terms of quality of image parameters. However, by using both manual and automatic image description parameters at the same time, diagnostic performance can be significantly improved with respect to the results of clinical practice.
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Kukar, M., Šajn, L., Grošelj, C., Grošelj, J. (2007). Multi-resolution Image Parametrization in Stepwise Diagnostics of Coronary Artery Disease. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_13
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DOI: https://doi.org/10.1007/978-3-540-73599-1_13
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