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Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier.

Ryo Asaoka1, Hiroshi Murata1, Aiko Iwase2

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A deep learning method accurately differentiates preperimetric open-angle glaucoma visual fields from healthy ones. This deep feed-forward neural network approach shows high potential for early glaucoma detection.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Open-angle glaucoma (OAG) is a leading cause of irreversible blindness.
  • Early detection of OAG is crucial for timely intervention and vision preservation.
  • Preperimetric glaucoma visual fields (PPGVFs) represent an early stage of the disease, often challenging to distinguish from healthy eyes.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) method for differentiating visual fields (VFs) of preperimetric open-angle glaucoma (OAG) patients from those of healthy eyes.
  • To assess the diagnostic accuracy of a deep feed-forward neural network (FNN) compared to other machine learning (ML) methods for PPGVF detection.

Main Methods:

  • A cohort study analyzed 171 PPGVFs from 53 eyes of OAG patients and 108 VFs from 87 healthy participants.
  • Visual field data (total deviation, mean deviation, pattern standard deviation) were used as input for DL and ML classifiers.
  • Classifiers included a deep feed-forward neural network (FNN), random forests (RF), gradient boosting, support vector machine, and neural network (NN).

Main Results:

  • The deep FNN classifier achieved a significantly higher area under the receiver operating characteristic curve (AUC) of 92.6% (95% CI, 89.8%-95.4%).
  • This performance was superior to other ML methods, including RF (79.0%), gradient boosting (77.6%), support vector machine (71.2%), and NN (66.7%).

Conclusions:

  • Preperimetric glaucoma visual fields can be distinguished from healthy visual fields with very high accuracy using a deep FNN classifier.
  • The DL approach demonstrates significant potential for improving early diagnosis and management of open-angle glaucoma.