Obstructive coronary artery disease diagnostics: machine learning approach for an effective preselection of patients

Mateusz Krysiński; Małgorzata Krysińska; Ewaryst Tkacz

doi:10.1016/B978-0-12-822706-0.00006-8

Obstructive coronary artery disease diagnostics: machine learning approach for an effective preselection of patients

Mateusz Krysiński
, Małgorzata Krysińska
, Ewaryst Tkacz

Department of Medical Informatics and Artificial Intelligence (RIB/RIB1)

Medical University of Silesia in Katowice

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Most patients undergoing diagnostic imaging of coronary artery disease do not show significant coronary artery stenosis. Despite this, they are exposed to the negative effects related to the radiation and administration of the contrast agent. An effective preselection of patients for further diagnostics may be the solution. There are reports of machine learning algorithms adapted to perform such a task. In this chapter we briefly introduce the reader to the problem of obstructive coronary artery disease and do our best to explain how to develop such an algorithm. The work is based on clinical data and diagnostic results collected at Silesian Center for Heart Diseases in Zabrze, Poland.

Original language	English
Title of host publication	Cardiovascular and Coronary Artery Imaging
Subtitle of host publication	Volume 1
Publisher	Elsevier
Pages	219-244
Number of pages	26
ISBN (Electronic)	9780128227060
ISBN (Print)	9780128227077
DOIs	https://doi.org/10.1016/B978-0-12-822706-0.00006-8
Publication status	Published - 1 Jan 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Obstructive coronary artery disease
algorithm development
coronary artery calcium scan
coronary computed tomography angiography
machine learning

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1016/B978-0-12-822706-0.00006-8

Cite this

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abstract = "Most patients undergoing diagnostic imaging of coronary artery disease do not show significant coronary artery stenosis. Despite this, they are exposed to the negative effects related to the radiation and administration of the contrast agent. An effective preselection of patients for further diagnostics may be the solution. There are reports of machine learning algorithms adapted to perform such a task. In this chapter we briefly introduce the reader to the problem of obstructive coronary artery disease and do our best to explain how to develop such an algorithm. The work is based on clinical data and diagnostic results collected at Silesian Center for Heart Diseases in Zabrze, Poland.",

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AU - Krysiński, Mateusz

AU - Krysińska, Małgorzata

AU - Tkacz, Ewaryst

PY - 2021/1/1

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AB - Most patients undergoing diagnostic imaging of coronary artery disease do not show significant coronary artery stenosis. Despite this, they are exposed to the negative effects related to the radiation and administration of the contrast agent. An effective preselection of patients for further diagnostics may be the solution. There are reports of machine learning algorithms adapted to perform such a task. In this chapter we briefly introduce the reader to the problem of obstructive coronary artery disease and do our best to explain how to develop such an algorithm. The work is based on clinical data and diagnostic results collected at Silesian Center for Heart Diseases in Zabrze, Poland.

KW - Obstructive coronary artery disease

KW - algorithm development

KW - coronary artery calcium scan

KW - coronary computed tomography angiography

KW - machine learning

UR - https://www.scopus.com/pages/publications/85138346507

U2 - 10.1016/B978-0-12-822706-0.00006-8

DO - 10.1016/B978-0-12-822706-0.00006-8

M3 - Chapter

AN - SCOPUS:85138346507

SN - 9780128227077

SP - 219

EP - 244

BT - Cardiovascular and Coronary Artery Imaging

PB - Elsevier

ER -

Obstructive coronary artery disease diagnostics: machine learning approach for an effective preselection of patients

Abstract

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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