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Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

Siamak Yousefi1, Taichi Kiwaki2, Yuhui Zheng2

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A new machine learning index detects glaucoma progression earlier than traditional methods. This approach improves early detection of vision loss in glaucoma patients, aiding timely intervention.

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Area of Science:

  • Ophthalmology
  • Medical technology
  • Artificial intelligence in healthcare

Background:

  • Standard automated perimetry indices for glaucoma lack sensitivity to localized vision loss or are highly variable.
  • Existing region-wise and point-wise indices offer intermediate sensitivity and variability.
  • Early and accurate detection of glaucoma progression is crucial for patient management.

Purpose of the Study:

  • To develop and evaluate a novel machine learning-based index for detecting glaucoma progression.
  • To compare the performance of the machine learning index against global, region-wise, and point-wise indices.

Main Methods:

  • Machine learning algorithms were employed to identify glaucoma progression patterns using visual field data from 2085 eyes.
  • A test-retest dataset of 133 eyes was used to optimize parameters for a 95% specificity threshold.
  • An independent dataset of 270 eyes and survival analysis were utilized for comparative method evaluation.

Main Results:

  • The machine learning index detected progression in 25% of eyes in 3.5 years, significantly faster than global (5.2 years), region-wise (4.5 years), and point-wise (3.9 years) indices.
  • Even after including confirmation visits, the machine learning analysis remained the fastest method for detecting progression.
  • Machine learning analysis demonstrated a consistent ability to detect progressing eyes earlier, including slowly progressing cases.

Conclusions:

  • Machine learning analysis offers a superior method for early glaucoma progression detection compared to existing indices.
  • The developed machine learning index consistently outperforms traditional methods in identifying visual field changes.
  • This advancement holds promise for earlier intervention and improved management of glaucoma patients.