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Kurysheva N.I.

Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan;
Ophthalmological Center of the Federal Medical-Biological Agency at the Federal Biophysical Center named after A.I. Burnazyan

Rodionova O.Ye.

N.N. Semenov Federal Research Center for Chemical Physics

Pomerantsev A.L.

N.N. Semenov Federal Research Center for Chemical Physics

Sharova G.A.

Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan;
OOO Glaznaya Klinika Doktora Belikovoy

Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma

Authors:

Kurysheva N.I., Rodionova O.Ye., Pomerantsev A.L., Sharova G.A.

More about the authors

Journal: Russian Annals of Ophthalmology. 2024;140(4): 80‑85

DOI: 10.17116/oftalma202414004180

Read: 1452 times

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Table of contents

APPLICATION OF ARTIFICIAL INTELLIGENCE IN GLAUCOMA. PART 2. NEURAL NETWORKS AND MACHINE LEARNING IN THE MONITORING AND TREATMENT OF GLAUCOMA
APPLICATION OF ARTIFICIAL INTELLIGENCE IN GLAUCOMA. PART 2. NEURAL NETWORKS AND MACHINE LEARNING IN THE MONITORING AND TREATMENT OF GLAUCOMA

To cite this article:

Kurysheva NI, Rodionova OYe, Pomerantsev AL, Sharova GA. Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma. Russian Annals of Ophthalmology. 2024;140(4):80‑85. (In Russ.)
https://doi.org/10.17116/oftalma202414004180

Подписка 2026 Вестник офтальмологии (Russian Annals of Ophthalmology)
 
МСФ на ЯМ
Использование инфографики авторами научных работ
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Abstract:

The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treating glaucoma provides information on how AI methods enhance the effectiveness of glaucoma monitoring and treatment, presents technologies that use machine learning, including neural networks, to predict disease progression and determine the need for anti-glaucoma surgery. The article also discusses the methods of personalized treatment based on projection machine learning methods and outlines the problems and prospects of using AI in solving tasks related to screening, diagnosing, and treating glaucoma.

Keywords:

artificial intelligence
machine learning
convolutional neural networks
deep learning
DD-SIMCA method
glaucoma
anterior segment optical coherence tomography

Authors:

Kurysheva N.I.

Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan;
Ophthalmological Center of the Federal Medical-Biological Agency at the Federal Biophysical Center named after A.I. Burnazyan

Rodionova O.Ye.

N.N. Semenov Federal Research Center for Chemical Physics

Pomerantsev A.L.

N.N. Semenov Federal Research Center for Chemical Physics

Sharova G.A.

Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan;
OOO Glaznaya Klinika Doktora Belikovoy

Received:

24.01.2024

Accepted:

01.04.2024

Close metadata

References:

  1. George R, Panda S, Vijaya L. Blindness in glaucoma: primary open-angle glaucoma versus primary angle-closure glaucoma-a meta-analysis. Eye (Lond). 2022;36(11):2099-2105. https://doi.org/10.1038/s41433-021-01802-9
  2. Choi B, Lin T, Mcllraith I, Hutnik C, Malvankar-Mehta MS. Anxiety, depression, and sleep-related outcomes of glaucoma patients: systematic review and meta-analysis. Can J Ophthalmol. 2023;58(4):346-355.  https://doi.org/10.1016/j.jcjo.2022.02.010
  3. Jaumandreu L, Antón A, Pazos M, et al. Glaucoma progression. Clinical practice guide. Arch Soc Esp Oftalmol (Engl Ed). 2023;98(1):40-57.  https://doi.org/10.1016/j.oftale.2022.08.003
  4. Dixit A, Yohannan J, Boland MV. Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data. Ophthalmology. 2021;128(7):1016-1026. https://doi.org/10.1016/j.ophtha.2020.12.020
  5. Shon K, Sung KR, Shin JW. Can Artificial Intelligence Predict Glaucomatous Visual Field Progression? A Spatial-Ordinal Convolutional Neural Network Model. Am J Ophthalmol. 2022;233:124-134.  https://doi.org/10.1016/j.ajo.2021.06.025
  6. Chen A, Montesano G, Lu R, Lee CS, Crabb DP, Lee AY. Visual Field Endpoints for Neuroprotective Trials: A Case for AI-Driven Patient Enrichment. Am J Ophthalmol. 2022;243:118-124.  https://doi.org/10.1016/j.ajo.2022.07.013
  7. Wen JC, Lee CS, Keane PA, et al. Forecasting future Humphrey Visual Fields using deep learning. PLoS One. 2019;14(4):e0214875. https://doi.org/10.1371/journal.pone.0214875
  8. Park K, Kim J, Lee J. Visual Field Prediction using Recurrent Neural Network [published correction appears in: Sci Rep. 2019 Sep 6;9(1):13173]. Sci Rep. 2019;9(1):8385. https://doi.org/10.1038/s41598-019-44852-6
  9. Garway-Heath DF, Zhu H, Cheng Q, et al. Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study. Health Technol Assess. 2018;22(4):1-106.  https://doi.org/10.3310/hta22040
  10. Kurysheva NI, Shatalova EO. Parafoveal vessel Density Dropout May Predict Glaucoma Progression in The Long-Term Follow Up. J Ophthalmol Res. 2022;5(4):150-166.  https://doi.org/10.26502/fjor.2644-00240073
  11. Kurysheva NI, Lepeshkina LV. Detection of Primary Angle Closure Glaucoma Progression by Optical Coherence Tomography. J Glaucoma. 2021; 30(5):410-420.  https://doi.org/10.1097/IJG.0000000000001829
  12. Hussain S, Chua J, Wong D, et al. Predicting glaucoma progression using deep learning framework guided by generative algorithm. Sci Rep. 2023; 13(1):19960. https://doi.org/10.1038/s41598-023-46253-2
  13. Kim D, Seo SB, Park SJ, Cho HK. Deep learning visual field global index prediction with optical coherence tomography parameters in glaucoma patients. Sci Rep. 2023;13(1):18304. https://doi.org/10.1038/s41598-023-43104-y
  14. Chauhan BC, Burgoyne CF. From clinical examination of the optic disc to clinical assessment of the optic nerve head: a paradigm change. Am J Ophthalmol. 2013;156(2):218-227.e2.  https://doi.org/10.1016/j.ajo.2013.04.016
  15. Mariottoni EB, Datta S, Shigueoka LS, et al. Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT. Ophthalmol Glaucoma. 2023;6(3):228-238.  https://doi.org/10.1016/j.ogla.2022.11.004
  16. Lin TPH, Hui HYH, Ling A, et al. Risk of Normal Tension Glaucoma Progression From Automated Baseline Retinal-Vessel Caliber Analysis: A Prospective Cohort Study. Am J Ophthalmol. 2023;247:111-120.  https://doi.org/10.1016/j.ajo.2022.09.015
  17. Li F, Su Y, Lin F, et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J Clin Invest. 2022;132(11): e157968. https://doi.org/10.1172/JCI157968
  18. Mohammadzadeh V, Wu S, Davis T, et al. Prediction of visual field progression with serial optic disc photographs using deep learning. Br J Ophthalmol. 2023:bjo-2023-324277. Epub ahead of print. https://doi.org/10.1136/bjo-2023-324277
  19. Tao S, Ravindranath R, Wang SY. Predicting Glaucoma Progression to Surgery with Artificial Intelligence Survival Models. Ophthalmol Sci. 2023; 3(4):100336. https://doi.org/10.1016/j.xops.2023.100336
  20. Jalamangala Shivananjaiah SK, Kumari S, Majid I, Wang SY. Predicting near-term glaucoma progression: An artificial intelligence approach using clinical free-text notes and data from electronic health records. Front Med (Lausanne). 2023;10:1157016. https://doi.org/10.3389/fmed.2023.1157016
  21. Wang R, Bradley C, Herbert P, et al. Deep learning-based identification of eyes at risk for glaucoma surgery. Sci Rep. 2024;14(1):599.  https://doi.org/10.1038/s41598-023-50597-0
  22. Chuprov AD, Borshchuk EL, Begun DN, et al. Method for assessing the need and type of reoperation in glaucoma surgical treatment using an artificial neural network. Fyodorov Journal of Ophthalmic Surgery = Oftal’mokhirurgiya. 2022;(4S):40-50. (In Russ.). https://doi.org/10.25276/0235-4160-2022-4S-40-50
  23. Shah D, Yan L, Saha R, et al. Predicting glaucoma interventions with deep learning networks. Invest Ophthalmol Vis Sci. 2020;61(7):4551.
  24. Kurysheva NI, Pomerantsev AL, Rodionova OYe, Sharova GA. Machine Learning Methods in the Comparative Evaluation of Various Approaches to the Surgical Treatment of Primary Angle Closure. Ophthalmology in Russia = Oftal’mologiya. 2022;19(3):549-556. (In Russ.). https://doi.org/10.18008/1816-5095-2022-3-549-556
  25. Rodionova O, Kurysheva N, Sharova G, Pomerantsev A. Expanding the DD-SIMCA concept: A case study of precision medicine. Anal Chim Acta. 2023;1250:340958. https://doi.org/10.1016/j.aca.2023.340958
  26. Kurysheva NI, Pomerantsev AL, Rodionova OY, Sharova GA. Comparison of Lens Extraction Versus Laser Iridotomy on Anterior Segment, Choroid, and Intraocular Pressure in Primary Angle Closure Using Machine Learning. J Glaucoma. 2023;32(6):e43-e55.  https://doi.org/10.1097/IJG.0000000000002145
  27. Kurysheva NI, Rodionova OYe, Pomerantsev AL, Sharova GA. Comparative study of predictors of hypotensive efficacy of laser peripheral iridotomy and lensectomy in patients with primary anterior chamber angle closure based on machine learning methods. Biomed Sign Proc Control. 2023;85:104884. https://doi.org/10.1016/j.bspc.2023.104884
  28. Kurysheva NI, Rodionova OY, Pomerantsev AL, Sharova GA, Golubnitschaja O. Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy. EPMA J. 2023;14(3):527-538.  https://doi.org/10.1007/s13167-023-00337-1
  29. Patent RF na izobretenie №2797850/08.06.2023. Byul. №16. Kurysheva NI, Rodionova OYe, Pomerantsev AL, Sharova GA. Method of choosing the tactics of surgical treatment of patients with primary closure of the angle of the anterior chamber of the eye. Accessed -1.02.2024. (In Russ.). https://new.fips.ru/registers-doc-view/fips_servlet
  30. Song MK, Sung KR, Shin JW, Jo YH, Won HJ. Glaucomatous progression after lens extraction in primary angle closure disease spectrum. J Glaucoma. 2020;29(8):711-717.  https://doi.org/10.1097/IJG.0000000000001537
  31. Bo J, Changulani T, Cheng ML, Tatham AJ. Outcome following laser peripheral iridotomy and predictors of future lens extrac- tion. J Glaucoma. 2018;27(3):275-280.  https://doi.org/10.1097/IJG.0000000000000863.
  32. Lin KY, Urban G, Yang MC, et al. Accurate Identification of the Trabecular Meshwork under Gonioscopic View in Real Time Using Deep Learning. Ophthalmol Glaucoma. 2022;5(4):402-412.  https://doi.org/10.1016/j.ogla.2021.11.003
  33. Garcia Nespolo R, Yi D, Cole E, Valikodath N, Luciano C, Leiderman YI. Evaluation of Artificial Intelligence-Based Intraoperative Guidance Tools for Phacoemulsification Cataract Surgery. JAMA Ophthalmol. 2022;140(2): 170-177.  https://doi.org/10.1001/jamaophthalmol.2021.5742
  34. Chaurasia AK, Greatbatch CJ, Hewitt AW. Diagnostic Accuracy of Artificial Intelligence in Glaucoma Screening and Clinical Practice. J Glaucoma. 2022;31(5):285-299.  https://doi.org/10.1097/IJG.0000000000002015
  35. Jeong JJ, Tariq A, Adejumo T, Trivedi H, Gichoya JW, Banerjee I. Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation. J Digit Imaging. 2022;35(2):137-152.  https://doi.org/10.1007/s10278-021-00556-w
  36. Beede E., Baylor E., Hersch F, et al. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. In: CHI Conference on Human Factors in Computing Systems (CHI ‘20), April 25—30, 2020, Honolulu, HI, USA. ACM, New York, NY, USA. 13 Pages. https://doi.org/10.1145/3313831.3376718
  37. Li F, Yang Y, Sun X, et al. Digital Gonioscopy Based on Three-dimensional Anterior-Segment OCT: An International Multicenter Study. Ophthalmology. 2022;129(1):45-53.  https://doi.org/10.1016/j.ophtha.2021.09.018
  38. Ball P. Is AI leading to a reproducibility crisis in science?. Nature. 2023; 624(7990):22-25.  https://doi.org/10.1038/d41586-023-03817-6
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