Evaluation of the Fractional Flow Reserve by Computer Tomography Data: Comparison of the Calculated Parameters with the Results of Invasive Measurements

Веселова, Т. Н.; Ternovoy, S. K.; Chepovskiy, Andrey; Borisenko, V. A.; Gavrilov, A. V.; Благосклонова, Е. Р.; Долотова, Д. Д.; Миронов, В. М.; Arutyunyan, G. K.

doi:10.18087/cardio.2021.7.n1540

Cited by 3 publications

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References 21 publications

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“…В проведенной нами работе для расчета ФРК КТ использовали ранее разработанную методику, основанную на построении трехмерной математической модели коронарного русла [6]. Полученные результаты корреляционного анализа показали хорошую сопоставимость ФРК КТ и ФРК ИНВ , что соответствует данным исследования, проведенного на другой группе пациентов со стенозами КА 50-75%, где коэффициент Пирсона составил 0,97 [11].…”

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Evaluation of functional significance of coronary arteries stenoses with estimation of fractional flow reserve based on computed tomography angiography data

Balakhonova,

Veselova,

Sukhinina

et al. 2024

jour

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Section: Discussionunclassified

“…Пример построения цветной математической модели коронарного кровотока для расчета ФРККТ представлен в нашей предыдущей работе [11].…”

Section: Original Articleunclassified

Evaluation of functional significance of coronary arteries stenoses with estimation of fractional flow reserve based on computed tomography angiography data

Balakhonova,

Veselova,

Sukhinina

et al. 2024

jour

Self Cite

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Computed tomography in diagnosis of coronary atherosclerosis and its complications

Balakhonova,

Sukhinina,

Veselova

et al. 2024

Kardio. vestn.

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Prospects for using machine learning to improve coronary angiography

Trusov,

Vildanova,

Zagitova

et al. 2024

HERALD of North-Western State Medical University named after I.

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Cardiovascular diseases pose the main threat to the population health of the Russian Federation and rank the first among the causes of death. Coronary heart disease has the highest standardized mortality rates among the population of the Russian Federation. Comprehensive diagnosis of coronary artery disease includes assessment of coronary atherosclerosis using both non-invasive methods, such as multispiral computed tomography of the coronary arteries, and invasive ones, including coronary angiography, and sometimes intravascular imaging. First two methods are the two most important diagnostic methods for coronary heart disease. The widespread use of medical technologies based on artificial intelligence in recent years has led to the emergence of new diagnostic and therapeutic opportunities. Artificial intelligence has bridged the gap between massive datasets and useful information by processing and analyzing important data at an unprecedented rate. The review identifies five potential cases with machine learning having significant prospects in the field of coronary angiography: improving quality and effectiveness, determining plaque characteristics, assessing hemodynamics, predicting disease outcomes and diagnosing non-atherosclerotic lesions of the coronary arteries. While machine learning has transformative potential in the field of coronary angiogram analysis, careful consideration of limitations, including data exchange protocols and interpretability of models is essential to fully exploit its potential and ensure optimal diagnosis and treatment of patients.

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Evaluation of the Fractional Flow Reserve by Computer Tomography Data: Comparison of the Calculated Parameters with the Results of Invasive Measurements

Cited by 3 publications

References 21 publications

Evaluation of functional significance of coronary arteries stenoses with estimation of fractional flow reserve based on computed tomography angiography data

Evaluation of functional significance of coronary arteries stenoses with estimation of fractional flow reserve based on computed tomography angiography data

Computed tomography in diagnosis of coronary atherosclerosis and its complications

Prospects for using machine learning to improve coronary angiography

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