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Identifying "preperimetric" glaucoma in standard automated perimetry visual fields.

Ryo Asaoka1, Aiko Iwase2, Kazunori Hirasawa3

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Machine learning can distinguish early open-angle glaucoma visual fields from healthy ones. This method aids in identifying preperimetric glaucoma visual fields, improving early detection and diagnosis.

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Area of Science:

  • Ophthalmology
  • Medical Technology
  • Machine Learning in Healthcare

Background:

  • Open-angle glaucoma (OAG) is a progressive optic neuropathy. Early detection is crucial for preventing vision loss. Preperimetric glaucoma visual fields (PPGVFs) represent an early stage before manifest visual field defects are apparent.
  • Differentiating PPGVFs from healthy visual fields (VFs) is challenging but essential for timely intervention.

Purpose of the Study:

  • To compare VFs of preperimetric OAG patients with those of healthy individuals.
  • To develop a machine learning model for discriminating between PPGVFs and healthy VFs.

Main Methods:

  • A dataset of 171 PPGVFs and 108 healthy VFs was analyzed using the Humphrey Field Analyzer 30-2 program.
  • The Random Forests machine learning algorithm was employed to classify VFs.
  • Predictors included 52 total deviation (TD) values, mean deviation (MD), and pattern standard deviation (PSD).

Main Results:

  • Significant differences were found in MD (P < 0.001) and PSD (P < 0.001) between healthy VFs and PPGVFs.
  • Significant differences in TD values were observed at 25 out of 52 test points (P < 0.001).
  • The Random Forests method achieved an area under the receiver operating characteristic curve (AROC) of 79.0% for discrimination.

Conclusions:

  • Distinct differences exist between VFs of healthy eyes and those with preperimetric glaucoma that progresses to manifest glaucoma.
  • The Random Forests classifier demonstrated effectiveness in distinguishing between these two groups of VFs.
  • This machine learning approach shows promise for early glaucoma detection.