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 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.

1. Shetty D. K., Talasila A., Shanbhag S., et al. Current state of artificial intelligence applications in ophthalmology and their potential to influence clinical practice., Cogent Engineering. 2021;8(1):1920707. DOI: 10.1080/23311916.2021.1920707

2. Garri D.D., Saakyan S.V., Khoroshilova-Maslova I.P., Tsygankov A.Yu., Nikitin O.I., Tarasov G.Yu. Мethods of Machine Learning in Ophthalmology: Review. Ophthalmology in Russia. 2020;17(1):20–31 (In Russ.). DOI: 10.18008/1816-5095-2020-1-20-31

3. Solli E., Dosh H., Tobias E., et. al. Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis. Translational vision science & technology. 2022;11(1):27. DOI: 10.1167/tvst.11.1.27

4. Li F., Wang Y., Xu, T., et al. Deep learning based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye. 2022;36:1433–1441. DOI: 10.1038/s41433-021-01552-8

5. Bowd C., Belghith A., Zangwill L.M., et al. Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes. American Journal of Ophthalmology. 2022;236:298–308. DOI: 10.1016/j.ajo.2021.11.008

6. Ran A.R., Tham C.C., Chan P.P., et al. Deep learning in glaucoma with optical coherence tomography: a review. Eye. 2021;35:188–201. DOI: 10.1038/s41433-020-01191-5

7. Teo Z.L., Tham Yih-Chung, Yu Marco, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta analysis. Ophthalmology. 2021;128(11):1580–1591. DOI: 10.1016/j.ophtha.2021.04.027

8. Munz I.V., Direev A.O., Gusarevich O.G., et al. Prevalence of ophthalmic diseases in the population older than 50 years. Vestnik Oftal’mologii. 2020;136(3):106–115 (In Russ.). DOI: 10.17116/oftalma2020136031106

9. Iomdina E.N., Bauer S.M., Kotliar K.E. Eye Biomechanics: theoretical aspects and clinical applications. Moscow: Real Time; 2015 (In Russ.).

10. National guidance of glaucoma / Egorov E.A., Erichev V.P., eds. Moscow: GEOTAR Media, 2019 (In Russ.).

11. Shamaev D.M., Luzhnov P.V., Iomdina E.N. Mathematical modeling of ocular pulse blood filling in rheoophthalmography. World Congress on Medical Physics and Biomedical Engineering 2018. Springer, Singapore. 2019:495–498. DOI: 10.1007/978-981-10-9035-6_91

12. Kadochkin Y.V., Luzhnov P.V., Iomdina E.N. Research of Motion Artefacts in Eye Blood Filling Diagnostics by Photoplethysmographic Method. In: Proc. of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020). P. 288–291 DOI: 10.5220/000917530288029

13. Kazakov S.B., Luzhnov P.V., Davydova I.D. Method for Quantitative Assessment of the Eyes Pulse Blood Flow with Linear Axisymmetric Model. BIODEVICES. 2021:239 242. https://www.scitepress.org/Papers/2021/103858/103858.pdf

14. Kiseleva A.A., Luzhnov P.V., Shamaev D.M. Verification of mathematical model for bioimpedance diagnostics of the blood flow in cerebral vessels. International Conference of Artificial Intelligence, Medical Engineering, Education. Springer, Cham. 2018:251–259. DOI: 10.1007/978-3-030-12082-5_23

15. Luzhnov P.V., Shamaev D.M., Kiseleva A.A., Iomdina E.N., Khoziev D.D., Kiseleva O.A. Using nonlinear dynamics for signal analysis in transpalpebral rheoophthalmography. Modern technologies in medicine = Sovremennyetehnologii v medicine. 2018;10(3):160–166 (In Russ.). DOI: 10.17691/stm2018.10.3.20

16. Short B. Selected aspects of ocular toxicity studies with a focus on high quality pathology reports: a pathology/toxicology consultant’s perspective. Toxicologic Pathology. 2021;49(3):673–699. DOI: 10.1177/0192623320946712

17. Zueva M.V. Fundamental ophthalmology: the role of electrophysiological studies. Vestnik Oftalmologii. 2014;130(6):28–36 (In Russ.).

18. Kazaykin V.N., Ponomarev V.O., Lizunov A.V., Zhdanov A.E., Dolganov A.Yu., Borisov V.I. The Current Role and Prospects of Electrophysiological Research Methods in Ophthalmology. Literature Review. Ophthalmology in Russia. 2020;17(4):669–675 (In Russ.). DOI: 10.18008/1816-5095-2020-4-669-675

19. Vincent A., Robson A.G., Holder G.E. Pathognomonic (Diagnostic) ERGs a Review and Update. Retina. 2013 Jan;33(1):5–12. DOI: 10.1097/IAE.0b013e31827e2306

20. Santos I.S., Linares Alba M.A., Rodríguez Reyes A.A., et al. Intravitreal bromfenac with liposomes. A toxicology study in rabbit eyes. A safety study in rabbit eyes. Exp Eye Res. 2020 May;194:108020. DOI: 10.1016/j.exer.2020.108020

21. Kulikov A.N., Nikolaenko E.N., Volkov V.V., Danilichev V.F. Electrogenesis of Retina and Optic Nerve after Vitrectomy for the Primary Full Thickness Macular Hole. Ophthalmology in Russia. 2019;16(1):46–55 (In Russ.). DOI: 10.18008/1816-5095-2019-1-46-55

22. Leocani L., Guerrieri S., Comi G. Visual evoked potentials as a biomarker in multiple sclerosis and associated optic neuritis. Journal of Neuro-Ophthalmology. 2018;38(3):350–357. DOI: 10.1097/wno.0000000000000704милан

23. Allam H.K., Soliman S., Wasfy T., et al. The neuro ophthalmological effects related to long term occupational exposure to organic solvents in painters. Toxicology and Industrial Health. 2018;34(2):91–98. DOI: 10.1177/0748233717736598

24. Gauvin M., Lina J.M., Lachapelle P. Advance in ERG analysis: from peak time and amplitude to frequency, power, and energy. BioMed research international. 2014;2014:246096. DOI: 10.1155/2014/246096

25. Johnson M.A. ISCEV extended protocol for the stimulus–response series for the dark adapted full field ERG b wave. Documenta Ophthalmologica. 2019;138(3):217–227. DOI: 10.1007/s10633-019-09687-6

26. Kirillova M.O., Zueva M.V., Tsapenko I.V., Zhuravleva A.N. Electrophysiological markers of preclinical diagnosis of glaucomatous optic neuropathy. Russian Ophthalmological Journal. 2021;14(1):35–41 (In Russ.). DOI: 10.21516/2072-0076-2021-14-1-35-41

27. Gubin D., Neroev V., Malishevskaya T., et al. Melatonin mitigates disrupted circadian rhythms, lowers intraocular pressure, and improves retinal ganglion cells function in glaucoma. Journal of Pineal Research. 2021;70(4):e12730. DOI: 10.1111/jpi.12730

28. Dewar J., Gray J. VII. On the Physiological Action of Light. Earth and Environmental Science Transactions of The Royal Society of Edinburgh. 1873;27(1):141–166.

29. Verdon W.A., Schneck M.E., Haegerstrom Portnoy G. A comparison of three techniques to estimate the human dark adapted cone electroretinogram. Vision research. 2003;43(19):2089–2099. DOI: 10.1016/S0042-6989(03)00330-4

30. Suetov A.A., Alekperov S.I., Odinokaya M.A., Kostina A.A., Petrova E.A. Multifocal Electroretinography as a Method of Functional Assessment of Retinal Laser Injury in Experimental Studies. Ophthalmology in Russia. 2021;18(1):110–116 (In Russ.). DOI: 10.18008/1816-5095-2021-1-110-116

31. Hoffmann M.B., Bach M., Kondo M. et al. ISCEV standard for clinical multifocal electroretinography (mfERG) (2021 update). Documenta Ophthalmologica. 2021;142(1):5–16. DOI: 10.1007/s10633-020-09812-w

32. Schröder P., Martínez Cañada P., Amorim A., et al. A Minimal Model Approach to Analyze Neuronal Circuit Dynamics from multifocal ERG (mERG). 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. 2019:2955–2958. DOI: 10.1109/embc.2019.8856840

33. Eremeev A.P., Ivliev S.A. Data Collection and Preparation of Training Samples for Problem Diagnosis of Vision Pathologies. Russian Conference on Artificial Intelligence. Springer, Cham. 2019:271–282. DOI: 10.1007/978-3-030-30763-9_23