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Related Experiment Video

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Predicting visual field global and local parameters from OCT measurements using explainable machine learning.

Md Mahmudul Hasan1, Jack Phu2,3,4,5, Henrietta Wang2,3,5

  • 1School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia. md_mahmudul.hasan@unsw.edu.au.

Scientific Reports
|February 16, 2025
PubMed
Summary
This summary is machine-generated.

New AI models predict visual field loss in glaucoma patients using optical coherence tomography (OCT) scans. This explainable AI tool aids diagnosis by correlating OCT data with visual field measures, improving patient care.

Keywords:
24-2 test gridExplainable machine learningGlaucomaOptical coherence tomographyPerimetrySHAP analysisVisual fields

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma causes progressive vision loss due to retinal ganglion cell damage, impacting visual fields.
  • Standard visual field testing can be challenging in some patients.
  • Optical coherence tomography (OCT) offers potential for predicting visual field (VF) measures, but remains difficult.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting VF measures from OCT data.
  • To enhance the interpretability of AI models using Shapley Additive exPlanations (SHAP).
  • To create a clinical software tool, OCT to VF Predictor, for multimodal glaucoma diagnosis.

Main Methods:

  • Five regression models were developed to predict VF measures using OCT data.
  • SHAP analysis was employed for model interpretability.
  • A dataset of 268 glaucomatous eyes and 226 normal eyes was used for evaluation.

Main Results:

  • Machine learning models demonstrated strong performance, outperforming previous deep learning studies.
  • Correlation coefficients reached 0.76 for mean deviation, 0.80 for visual field index, and 0.76 for pattern standard deviation.
  • Pointwise sensitivity prediction achieved a mean absolute error of 2.51 dB, and grayscale prediction yielded a 77% structural similarity index.

Conclusions:

  • The developed models accurately predict VF measures from OCT data.
  • SHAP analysis provides crucial insights into features relevant for glaucoma diagnosis.
  • The OCT to VF Predictor tool shows promise in assisting eye care practitioners with explainable AI.