Ophthalmic Bioengineering. Review

Ophthalmic Bioengineering. Review

This article published the materials of the round table “Bioengineering in ophthalmology” (OphthalmicBioengineering), held on May 13, 2021 as part of the international conference Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). USBEREIT is held under...

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Bibliographic Details
Main Authors: V. O. Ponomarev, A. E. Zhdanov, P. V. Luzhnov, I. D. Davydova, E. N. Iomdina, A. V. Lizunov, A. Yu. Dolganov, S. A. Ivliev, M. A. Znamenskaya, V. N. Kazajkin, V. I. Borisov, E. O. Filatova
Format: Article
Language:Russian
Published: Ophthalmology Publishing Group 2023-04-01
Series:Oftalʹmologiâ
Subjects:
fluctuations in ocular blood flow
intraocular pressure
reoophthalmography
hemodynamics
choroid volume
ocular blood flow
retinotoxicity
electrophysiological studies
electroretinography
erg
spectrogram
wavelet scalogram
fourier analysis
roland
tomey
binary format files
data extraction
oct
machine learning
Online Access:https://www.ophthalmojournal.com/opht/article/view/2036
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Summary:This article published the materials of the round table “Bioengineering in ophthalmology” (OphthalmicBioengineering), held on May 13, 2021 as part of the international conference Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). USBEREIT is held under the auspices of the IEEE Engineering in Medicine and Biology Society. The article presents reports on: metrological aspects of registration of tonometric and electrophysiological signals in ophthalmic diagnostics; approaches to modeling the processes of pulse blood filling of the eye with the determination of hemodynamic parameters; retinotoxicity based on electrophysiological signals; analysis of electrophysiological signals in the frequency-time domain and its application in clinical practice; extraction and analysis of specialized data obtained from the electrophysiological medical device; as well as diagnosing retinal diseases based on optical coherence tomography using machine learning.
ISSN:1816-5095
2500-0845

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