Possibilities of neural networks application in assessing the results of intravascular ultrasound (literature review)
Abstract
The review considers the experience of neural networks use in assessing the results of intravascular ultrasound investigation (detection of vulnerable plaques, extraction of the lumen and layers of the vessel wall, prediction of the fractional flow reserve).
About the Authors
P. G. Madonov
Novosibirsk State Medical University
Russian Federation
Russian Federation
Madonov Pavel G. — Dr. Sci. (Med.), Professor, Head, Department of Pharmacology, Clinical Pharmacology and Evidence-Based Medicine
Novosibirsk
L. D. Khidirova
Novosibirsk State Medical University
Russian Federation
Russian Federation
Khidirova Lyudmila D. — Dr. Sci. (Med.), Assistant Professor, Department of Pharmacology, Clinical Pharmacology and Evidence-Based Medicine
52, Krasny Prospect, Novosibirsk, 630091
E. A. Kovalev
Novosibirsk State Medical University
Russian Federation
Russian Federation
Kovalev Evgeniy A. — 6-year Student
Novosibirsk
References
1. Prajapati B.B., Parikh S.M., Pafel J.M. (2019). Effective healthcare services by IoT-based model of voluntary doctors. In Mishra D., Yang X.S., Unal A. (eds). Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies. Springer, Singapore, 16.
2. Yakhontov D.A. (2012). Evidence-based Medicine in Questions and Answers. Novosibirsk, 326 p. In Russ.
3. Benjamens S., Dhunnoo P., Meskó B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. npj Digit. Med., 3 (1): 118. doi: 10.1038/s41746-020-00324-0.
4. FDA. Artificial intelligence and machine learning in software as a medical devices? Retrieved on March 17, 2021 from https://www.fda.gov/medical-devices/softwaremedical-device-samd/artificial-intelligence-andmachine-learning-softwaremedical-device#regulation.
5. Mincholе A., Rodriguez B. (2019) Artificial intelligence for the electrocardiogram. Nat. Med., 25 (1), 22–23. doi: 10.1038/s41591-018-0306-1.
6. Bae Y., Kang S.J., Kim G. et al. (2019). Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning. Atherosclerosis, 288, 168–174. doi: 10.1016/j.atherosclerosis.2019.04.228.
7. Jun T.J., Kang S.J., Lee J.G. et al. (2019). Automated detection of vulnerable plaque in intravascular ultrasound images. Med. Biol. Eng. Comput., 57 (4), 863– 876. doi: 10.1007/s11517-018-1925-x.
8. Cao Y., Xiao X., Liu Z. et al. (2020). Detecting vulnerable plaque with vulnerability index based on convolutional neural networks. Comput. Med. Imaging Graph., 81, 101711. doi: 10.1016/j.compmedimag.2020.101711.
9. Lo Vercio L., Del Fresno M., Larrabide I. (2019). Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures. Comput. Methods Programs Biomed., 177, 113–121. doi: 10.1016/j.cmpb.2019.05.021.
10. Wang Y.Y., Qiu C.H., Jiang J., Xia S.R. (2019). Detecting the media-adventitia border in intravascular ultrasound images through a classification-based approach. Ultrason. Imaging, 41 (2), 78–93. doi: 10.1177/0161734618820112.
11. Yang J., Faraji M., Basu A. (2019). Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net. Ultrasonics, 96, 24–33.
12. Xia M., Yan W., Huang Y. et al. (2020). Extracting membrane borders in IVUS images using a multiscale feature aggregated U-Net. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 1650–1653. doi: 10.1109/EMBC44109.2020.9175970.
13. Papaioannou T.G., Schizas D., Vavuranakis M. et al. (2014). Quantification of new structural features of coronary plaques by computational post-hoc analysis of virtual histology-intravascular ultrasound images. Comput. Methods Biomech. Biomed. Engin., 17, 643– 651. doi: https://doi.org/10.1080/10255842.2012.713940.
14. Lee J.G., Ko J., Hae H. (2020). Intravascular ultrasound-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. Atherosclerosis, 292, 171–177.
Review
For citations:
Madonov P.G., Khidirova L.D., Kovalev E.A. Possibilities of neural networks application in assessing the results of intravascular ultrasound (literature review). Journal of Siberian Medical Sciences. 2021;(2):127-135. (In Russ.)
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