Prospects for using machine learning to improve coronary angiography
- Authors: Trusov Y.A.1, Vildanova A.A.2, Zagitova A.N.2, Simenenkova M.O.3, Settarova F.E.3, Rashitova Z.N.4, Kurchenko A.S.5, Lapshina Y.N.6, Romanova A.A.7, Nechaev K.M.7, Arkhipov R.A.3, Umerov A.R.3, Zainullin I.I.8, Bikmullina K.F.8
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Affiliations:
- Samara State Medical University
- Bashkir State Medical University
- V.I. Vernadsky Crimean Federal University
- Pirogov Russian National Research Medical University
- I.M. Sechenov First Moscow State Medical University (Sechenov University)
- Penza State University
- Saint Petersburg State Pediatric Medical University
- Izhevsk State Medical Academy
- Issue: Vol 16, No 2 (2024)
- Pages: 5-18
- Section: Reviews
- Submitted: 12.03.2024
- Accepted: 26.03.2024
- Published: 03.07.2024
- URL: https://journals.eco-vector.com/vszgmu/article/view/629024
- DOI: https://doi.org/10.17816/mechnikov629024
- ID: 629024
Cite item
Abstract
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.
Full Text
About the authors
Yurii A. Trusov
Samara State Medical University
Author for correspondence.
Email: secretplace@internet.ru
ORCID iD: 0000-0001-6407-3880
SPIN-code: 3203-5314
assistant
Russian Federation, SamaraAirina A. Vildanova
Bashkir State Medical University
Email: airinavildanowa@gmail.com
ORCID iD: 0009-0000-0625-9732
Russian Federation, Ufa
Amina N. Zagitova
Bashkir State Medical University
Email: zagitova.amina@mail.ru
ORCID iD: 0009-0006-9528-4019
Russian Federation, Ufa
Maria O. Simenenkova
V.I. Vernadsky Crimean Federal University
Email: masha.simenenkova@mail.ru
ORCID iD: 0009-0003-0523-3655
Russian Federation, Simferopol
Feride E. Settarova
V.I. Vernadsky Crimean Federal University
Email: ferideshka.settarova@gmail.com
ORCID iD: 0009-0003-5059-6105
Russian Federation, Simferopol
Zarina N. Rashitova
Pirogov Russian National Research Medical University
Email: rashitovazarina@yandex.ru
ORCID iD: 0009-0004-7890-5472
Russian Federation, Moscow
Anastasiia S. Kurchenko
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Email: kurchenko.anastasiia@yandex.ru
ORCID iD: 0009-0004-1055-3394
Russian Federation, Moscow
Yulia N. Lapshina
Penza State University
Email: jul1a110401@yandex.ru
ORCID iD: 0009-0001-0985-9212
SPIN-code: 2724-5472
Russian Federation, Penza
Anastasiia A. Romanova
Saint Petersburg State Pediatric Medical University
Email: romanna96@mail.ru
ORCID iD: 0009-0005-5675-835X
Russian Federation, Saint Petersburg
Konstantin M. Nechaev
Saint Petersburg State Pediatric Medical University
Email: kostanechaev16@gmail.com
ORCID iD: 0009-0005-6937-0215
Russian Federation, Saint Petersburg
Rodion A. Arkhipov
V.I. Vernadsky Crimean Federal University
Email: nomier@list.ru
ORCID iD: 0009-0004-3971-733X
Russian Federation, Simferopol
Akim R. Umerov
V.I. Vernadsky Crimean Federal University
Email: ufadime74@mail.ru
ORCID iD: 0009-0007-9134-1044
Russian Federation, Simferopol
Ildar I. Zainullin
Izhevsk State Medical Academy
Email: il116rus22@gmail.com
ORCID iD: 0009-0005-0812-6171
Russian Federation, Izhevsk
Kamila F. Bikmullina
Izhevsk State Medical Academy
Email: kbikmullina@mail.ru
ORCID iD: 0009-0003-2881-1876
Russian Federation, Izhevsk
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