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Evaluation of the effectiveness of the decision support algorithm for physicians in retinal dystrophy using machine learning methods
A.E. Zhdanov 1,2, A.Yu. Dolganov 1, D. Zanca 2, V.I. Borisov 1, E. Luchian 3, L.G. Dorosinsky 1

1 Ural Federal University named after the first President of Russia B.N.Yeltsin, Engineering School of Information
Technologies, Telecommunications and Control Systems, 620078, Yekaterinburg, Russia, Mira Str. 19;
2 University of Erlangen–Nuremberg, Machine Learning and Data Analytics (MaD) Lab,
91052, Germany, Erlangen, Carl-Thiersch-Straße 2b;
3 Polytechnic University of Bucharest, Faculty of Electrical Engineering,
060042, Romania, Bucharest, Splaiul Independentei 313

 PDF, 4033 kB

DOI: 10.18287/2412-6179-CO-1124

Pages: 272-277.

Full text of article: Russian language.

Abstract:
Electroretinography is a method of electrophysiological testing, which allows diagnosing diseases associated with disorders of the vascular structures of the retina. The classical analysis of the electroretinogram is based on assessing four parameters of the amplitude-time representation and often needs to be specified further using alternative diagnostic methods. This study proposes the use of an original physician decision support algorithm for diagnosing retinal dystrophy. The proposed algorithm is based on machine learning methods and uses parameters extracted from the wavelet scalogram of pediatric and adult electroretinogram signals. The study also uses a labeled database of pediatric and adult electroretinogram signals recorded using a computerized electrophysiological workstation EP-1000 (Tomey GmbH) at the IRTC Eye Microsurgery Ekaterinburg Center. The scientific novelty of this study consists in the development of special mathematical and algorithmic software for analyzing a procedure for extracting wavelet scalogram parameters of the electroretinogram signal using the cwt function of the PyWT. The basis function is a Gaussian wavelet of order 8. Also, the scientific novelty includes the development of an algorithm for analyzing electroretinogram signals that implements the classification of adult (pediatric) electroretinogram signals 19 (20) percent more accurately than classical analysis.

Keywords:
electroretinography, electroretinogram, ERG, electrophysiological study, EPS, retinal dystrophy, wavelet analysis, wavelet scalogram, decision trees, decision support algorithm.

Citation:
Zhdanov AE, Dolganov AY, Zanca D, Borisov VI, Lucian E, Dorosinskiy LG. Evaluation of the effectiveness of the decision support algorithm for physicians in retinal dystrophy using machine learning methods. Computer Optics 2023; 47(2): 272-277. DOI: 10.18287/2412-6179-CO-1124.

Acknowledgements:
The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged.

References:
  1. Gonzales-Turín JM, et al. Relationship between self-reported visual impairment and worsening frailty transition states in older people: a longitudinal study. Aging Clin Exp Res 2021; 33(9): 2491-2498. DOI: 10.1007/s40520-020-01768-w.
  2. Iodice F, Cassano V, Rossini PM. Direct and indirect neurological, cognitive, and behavioral effects of COVID-19 on the healthy elderly, mild-cognitive-impairment, and Alzheimer's disease populations. Neurol Sci 2021; 42(2): 455-465. DOI: 10.1007/s10072-020-04902-8.
  3. Van Schijndel NH, et al. The inverse problem in electroretinography: a study based on skin potentials and a realistic geometry model. IEEE. Trans Biomed Eng 1997; 44(2): 209-211. DOI: 10.1109/TBME.2021.3075617.
  4. Jonnal RS. Toward a clinical optoretinogram: a review of noninvasive, optical tests of retinal neural function. Ann Transl Med 2021; 9(15): 1270. DOI: 10.21037/atm-20-6440.
  5. Gauvin M, Little JM, Lina JM, Lachapelle P. Functional decomposition of the human ERG based on the discrete wavelet transform. Journal of vision 2015; 15(16): 14-14. DOI: 10.1167/15.16.14.
  6. Gauvin M, et al. Functional decomposition of the human ERG based on the discrete wavelet transform. J Vis 2015; 15(16): 14. DOI: 10.1167/15.16.14.
  7. Schröder P, et al. A minimal-model approach to analyze neuronal circuit dynamics from multifocal ERG (mERG). 2019 41st Annual Int Conf of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019: 2955-2958. DOI: 10.1109/EMBC.2019.8856840.
  8. Varadharajan S, Fitzgerald K, Lakshminarayanan V. Wavelet analysis of ERG of patients with Duchenne Muscular Dystrophy. Vision Science and its Application, OSA Technical Digest 2000: 65-68.
  9. Gauvin M, Lina JM, Lachapelle P. Advance in ERG analysis: from peak time and amplitude to frequency, power, and energy. BioMed Res Int 2014; 2014: 246096. DOI: 10.1155/2014/246096.
  10. Zhdanov AE, et al. OculusGraphy: Norms for electroretinogram signals. 2021 IEEE 22nd Int Conf of Young Professionals in Electron Devices and Materials (EDM) 2021: 399-402. DOI: 10.1109/EDM52169.2021.9507597.
  11. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979; 9(1): 62-66. DOI: 10.1109/TSMC.1979.4310076.
  12. Abbasi H, et al. 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 Int Conf of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019: 1825-1828. DOI: 10.1109/EMBC.2019.8857665.
  13. Shamshinova AM, Volkov VV. Functional methods of research in ophthalmology. Publishing House Medicine; 2004.
  14. McCulloch DL, et al. ISCEV Standard for full-field clinical electroretinography (2015 update).  Doc Ophthalmol 2015; 130(1): 1-12. DOI: 10.1007/s10633-014-9473-7.
  15. Lior R. Data mining with decision trees: theory and applications. World Scientific; 2014.
  16. Pedregosa F, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011; 12: 2825-2830.

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