Artificial neural networks in cardiology: analysis of graphic data
Aim. To consider application of convolutional neural networks for processing medical images in various fields of cardiology and cardiac surgery using publications from 2016 to 2019 as an example.Materials and methods. In the study, we used the following scientific databases: PubMed Central, ArXiv, R...
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| Language: | English |
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Siberian State Medical University (Tomsk)
2022-01-01
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| Series: | Бюллетень сибирской медицины |
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| Online Access: | https://bulletin.ssmu.ru/jour/article/view/4596 |
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| author | P. S. Onishchenko K. Yu. Klyshnikov E. A. Ovcharenko |
| author_facet | P. S. Onishchenko K. Yu. Klyshnikov E. A. Ovcharenko |
| author_sort | P. S. Onishchenko |
| collection | DOAJ |
| description | Aim. To consider application of convolutional neural networks for processing medical images in various fields of cardiology and cardiac surgery using publications from 2016 to 2019 as an example.Materials and methods. In the study, we used the following scientific databases: PubMed Central, ArXiv, ResearchGate. The cited publications were grouped by the area of interest (heart, aorta, carotid arteries).Results. The general principle of work of the technology under consideration was described, the results were shown, and the main areas of application of this technology in the studies under consideration were described. For most of the studies, sample sizes were given. The author’s view on the development of convolutional neural networks in medicine was presented and some limiting factors for their distribution were listed.Conclusion. A brief overview shows possible areas of application of convolutional neural networks in the fields of cardiology and cardiac surgery. Without denying the existing problems, this type of artificial neural networks may help many doctors and researchers in the future. |
| format | Article |
| id | doaj-art-8d4f1c847ae0403498cdebc7833bca8f |
| institution | Kabale University |
| issn | 1682-0363 1819-3684 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Siberian State Medical University (Tomsk) |
| record_format | Article |
| series | Бюллетень сибирской медицины |
| spelling | doaj-art-8d4f1c847ae0403498cdebc7833bca8f2025-08-20T03:38:06ZengSiberian State Medical University (Tomsk)Бюллетень сибирской медицины1682-03631819-36842022-01-0120419320410.20538/1682-0363-2021-4-193-2042855Artificial neural networks in cardiology: analysis of graphic dataP. S. Onishchenko0K. Yu. Klyshnikov1E. A. Ovcharenko2Research Institute for Complex Issues of Cardiovascular Diseases; Science Institute of Computational Technologies of the Siberian Branch of the Russian Academy of SciencesResearch Institute for Complex Issues of Cardiovascular DiseasesResearch Institute for Complex Issues of Cardiovascular DiseasesAim. To consider application of convolutional neural networks for processing medical images in various fields of cardiology and cardiac surgery using publications from 2016 to 2019 as an example.Materials and methods. In the study, we used the following scientific databases: PubMed Central, ArXiv, ResearchGate. The cited publications were grouped by the area of interest (heart, aorta, carotid arteries).Results. The general principle of work of the technology under consideration was described, the results were shown, and the main areas of application of this technology in the studies under consideration were described. For most of the studies, sample sizes were given. The author’s view on the development of convolutional neural networks in medicine was presented and some limiting factors for their distribution were listed.Conclusion. A brief overview shows possible areas of application of convolutional neural networks in the fields of cardiology and cardiac surgery. Without denying the existing problems, this type of artificial neural networks may help many doctors and researchers in the future.https://bulletin.ssmu.ru/jour/article/view/4596convolutional neural networkcnnffrcardiologycardiovascular diseasesstenosisdetection |
| spellingShingle | P. S. Onishchenko K. Yu. Klyshnikov E. A. Ovcharenko Artificial neural networks in cardiology: analysis of graphic data Бюллетень сибирской медицины convolutional neural network cnn ffr cardiology cardiovascular diseases stenosis detection |
| title | Artificial neural networks in cardiology: analysis of graphic data |
| title_full | Artificial neural networks in cardiology: analysis of graphic data |
| title_fullStr | Artificial neural networks in cardiology: analysis of graphic data |
| title_full_unstemmed | Artificial neural networks in cardiology: analysis of graphic data |
| title_short | Artificial neural networks in cardiology: analysis of graphic data |
| title_sort | artificial neural networks in cardiology analysis of graphic data |
| topic | convolutional neural network cnn ffr cardiology cardiovascular diseases stenosis detection |
| url | https://bulletin.ssmu.ru/jour/article/view/4596 |
| work_keys_str_mv | AT psonishchenko artificialneuralnetworksincardiologyanalysisofgraphicdata AT kyuklyshnikov artificialneuralnetworksincardiologyanalysisofgraphicdata AT eaovcharenko artificialneuralnetworksincardiologyanalysisofgraphicdata |