دورية أكاديمية

Formation of the pediatric electroretinogram database parameters for the development of doctor’s decisionmaking algorithm ; Формирование параметров педиатрической базы электроретинограммы для разработки алгоритма поддержки принятия решения врачом

التفاصيل البيبلوغرافية
العنوان: Formation of the pediatric electroretinogram database parameters for the development of doctor’s decisionmaking algorithm ; Формирование параметров педиатрической базы электроретинограммы для разработки алгоритма поддержки принятия решения врачом
المؤلفون: A. E. Zhdanov, A. Yu. Dolganov, V. N. Kazaykin, V. I. Borisov, V. O. Ponomarev, L. G. Dorosinsky, A. V. Lizunov, E. Luchian, X. Bao, А. Е. Жданов, А. Ю. Долганов, В. Н. Казайкин, В. И. Борисов, В. О. Пономарев, Л. Г. Доросинский, А. В. Лизунов, Е. Лучиан, С. Бао
المساهمون: В исследовании были использованы алгоритмы, выполненные при финансовой поддержке РФФИ в рамках научного проекта № 20-37-90037.
المصدر: Acta Biomedica Scientifica; Том 7, № 2 (2022); 190-198 ; 2587-9596 ; 2541-9420
بيانات النشر: Scientific Centre for Family Health and Human Reproduction Problems
سنة النشر: 2022
المجموعة: Acta Biomedica Scientifica
مصطلحات موضوعية: дерево решений, electroretinogram, ERG, electrophysiological study, EPS, wavelet analysis, wavelet scalogram, machine learning, decision tree, электроретинограмма, ЭРГ, электрофизиологическое исследование, ЭФИ, вейвлет-анализ, вейвлетскалограмма, машинное обучение
الوصف: Electroretinography is a non-invasive electrophysiological method standardized by the International Society for Clinical Electrophysiology of Vision (ISCEV). Electroretinography has been used for the clinical application and standardization of electrophysiological protocols for diagnosing the retina since 1989. Electroretinography become fundamental ophthalmological research method that may assesses the state of the retina. To transfer clinical practice to patients the establishment of standardized protocols is an important step. It is important for monitoring successful molecular therapy in retinal degeneration. Retinitis pigmentosa or achromatopsia and, consequently, affected cones or rods photoreceptors is corresponded to complete absent of electrical response. Thus, detection of even modest improvements after therapeutic treatment is required. Standardized protocols allow the implementation of electroretinography under conditions of optimization of sensitivity and specificity during clinical trials. It should be noted that the literature on retinal diseases demonstrates clinical cases in which patients may have several retinal diseases at the same time. In such cases, it is necessary to detect a group of characteristics of electrophysiological signals with high accuracy to improve the application of various diagnostic solutions. The classification of electroretinogram signals depends on the quality of labeled biomedical information or databases, in addition to this, the accuracy of the classification results obtained depends not only on computer technology, but also on the quality of the input data. To date, the analysis of electroretinogram signals is realized manually and largely depends on the experience of clinicians. The development of automated algorithms for analyzing electroretinogram signals may simplify routine processes and improve the quality of diagnosing eye diseases. This article describes the formation of the parameters of pediatric electroretinogram database parameters for the development of ...
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf
اللغة: Russian
العلاقة: https://www.actabiomedica.ru/jour/article/view/3441/2334Test; Verdon WA, Schneck ME, Haegerstrom-Portnoy G. A comparison of three techniques to estimate the human dark-adapted cone electroretinogram. Vision Res. 2003; 43(19): 2089-2099. doi:10.1016/s0042-6989(03)00330-4; Brigell M, Jeffrey BG, Mahroo OA, Tzekov K. ISCEV extended protocol for derivation and analysis of the strong flash rod-isolated ERG a-wave. Doc Ophthalmol. 2020; 140(1): 5-12. doi:10.1007/s10633-019-09740-4; Xiaofan J, Bhatti T, Tariq A, Ting Sh, Williams K, Hysi PG, et al. The rise-time of the rod-driven electroretinogram a-wave measured in over 200 twins: Association with age and estimation of heritability. Invest Ophthalmol. Vis Sci. 2021; 62(8): 617.; Hébert M, Mérette Ch, Gagné AM, Paccalet Th, Moreau I, Lavoie J, et al. The electroretinogram may differentiate schizophrenia from bipolar disorder. Biol Psychiatry. 2020; 87(3): 263-270. doi:10.1016/j.biopsych.2019.06.014; Akula JD, Lyubarsky AL, Naarendorp F. The sensitivity and spectral identity of the cones driving the b-wave of the rat electroretinogram. Vis Neurosci. 2003; 20(2): 109-117. doi:10.1017/s0952523803202029; Lingley AJ, Kantungane A-L, Coupland SG. Comparison of the uniform-field electroretinogram and the pattern electroretinogram to checkerboard and bar gratings. Doc Ophthalmol. 2020; 140(1): 13-21. doi:10.1007/s10633-019-09714-6; Viswanathan S, Frishman LJ, Robson JG, Walters JW. The photopic negative response of the flash electroretinogram in primary open angle glaucoma. Invest Ophthal Vis Sci. 2001; 42(2): 514-522.; Thompson DA, Fujinami K, Perlman I, Hamilton R, Robson AG. ISCEV extended protocol for the dark-adapted red flash ERG. Doc Ophthalmol. 2018; 136(3): 191-197. doi:10.1007/s10633-018-9644-z; Frishman L, Sustar M, Kremers J, McAnany JJ, Sarossy M, Tzekov R, et al. ISCEV extended protocol for the photopic negative response (PhNR) of the full-field electroretinogram. Doc Ophthalmol. 2018; 136(3): 207-211. doi:10.1007/s10633-018-9638-x; Youssef P, Nath S, Chaimowitz GA, Prat SS. Electroretinography in psychiatry: A systematic literature review. Eur Psychiatry. 2019; 62: 97-106. doi:10.1016/j.eurpsy.2019.09.006; Еремеев А.П., Ивлиев С.А. Разработка базы данных и конвертера для извлечения и анализа специализированных данных, получаемых с медицинского аппарата. Программные продукты и системы. 2019; 3(32): 512-517. doi:10.15827/0236-235X.127.512-517; Zhdanov AE, Dolganov AYu, Kazajkin VN, Ponomarev VO, Lizunov AV, Borisov VI, et al. OculusGraphy: Literature review on electrophysiological research methods in ophthalmology and electroretinograms processing using wavelet transform. 2020 International Conference on e-Health and Bioengineering (EHB). 2020: 1-6, doi:10.1109/EHB50910.2020.9280221; Zhdanov AE, Borisov VI, Dolganov AY, Lucian E, Bao X, Kazaijkin VN. OculusGraphy: Norms for electroretinogram signals. 2021 IEEE 22nd International Conference of Young Professionals in Electron Devices and Materials (EDM). 2021: 399-402. doi:10.1109/EDM52169.2021.9507597; Zhdanov AE, Borisov VI, Dolganov AY, Lucian E, Bao X, Kazaijkin VN. OculusGraphy: Filtering of electroretinography response in adults. 2021 IEEE 22nd International Conference of Young Professionals in Electron Devices and Materials (EDM). 2021: 395-398. doi:10.1109/EDM52169.2021.9507654; Abbasi H, Bennet L, Gunn AJ, Unsworth ChP. 2D wavelet scalogram training of deep convolutional neural network for automatic identification of micro-scale sharp wave biomarkers in the hypoxic-ischemic EEG of preterm sheep. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019: 1825-1828. doi:10.1109/EMBC.2019.8857665; https://www.actabiomedica.ru/jour/article/view/3441Test
DOI: 10.29413/ABS.2022-7.2.20
الإتاحة: https://doi.org/10.29413/ABS.2022-7.2.20Test
https://doi.org/10.1016/s0042-6989Test(03)00330-4
https://doi.org/10.1007/s10633-019-09740-4Test
https://doi.org/10.1016/j.biopsych.2019.06.014Test
https://doi.org/10.1017/s0952523803202029Test
https://doi.org/10.1007/s10633-019-09714-6Test
https://doi.org/10.1007/s10633-018-9644-zTest
https://doi.org/10.1007/s10633-018-9638-xTest
https://doi.org/10.1016/j.eurpsy.2019.09.006Test
https://doi.org/10.15827/0236-235X.127.512-517Test
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رقم الانضمام: edsbas.726F709C
قاعدة البيانات: BASE