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Prediction of visual field progression in glaucoma: existing methods and artificial intelligence

  • Forefront Review
  • Organizer: Tetsuya Yamamoto, MD
  • Published:
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Abstract

Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable. There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.

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Funding

Grants (nos. 19H01114: RA, 18KK0253: RA, 20K09784: RA, and 80635748: HM) from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and the Translational Research Program; Strategic Promotion for practical application of Innovative medical Technology (TR-SPRINT) from the Japan Agency for Medical Research and Development (AMED) (RA), and the Japan Glaucoma Society Research Project Support Program.

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  1. Department of Ophthalmology, Seirei Hamamatsu General Hospital, 2-12-12 Sumiyoshi, Naka-ku, Hamamatsu, Shizuoka, Japan

    Ryo Asaoka

  2. Seirei Christopher University, Hamamatsu, Shizuoka, Japan

    Ryo Asaoka

  3. The Graduate School for the Creation of New Photonics Industries, Hamamatsu, Shizuoka, Japan

    Ryo Asaoka

  4. Department of Ophthalmology, National Center for Global health and Medicine, Tokyo, Japan

    Hiroshi Murata

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  1. Ryo Asaoka
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Asaoka, R., Murata, H. Prediction of visual field progression in glaucoma: existing methods and artificial intelligence. Jpn J Ophthalmol 67, 546–559 (2023). https://doi.org/10.1007/s10384-023-01009-3

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