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    Unsupervised machine learning identifies patterns of glaucoma-related binocular visual field loss

    VIM successfully identified binocular patterns of glaucomatous defects in SAP visual fields

     

    San Diego—In previous research, Christopher Bowd, PhD, and colleagues at the University of California, San Diego, Hamilton Glaucoma Center, demonstrated that an unsupervised machine-learning based classifier—the variational Bayesian independent component analysis mixture model (VIM)—could be applied to monocular standard automated perimetry (SAP) visual fields and monocular frequency-doubling technology (FDT) perimetry visual fields to identify patterns of glaucomatous damage without foreknowledge of diagnosis and without human intervention.

    Changes in these patterns over time proved to be a promising method for describing glaucomatous progression, and according to recent evidence, VIM, trained on monocular SAP visual fields, was better at detecting glaucomatous progression than current methods.

    Taking their investigations forward, they determined that VIM successfully identified binocular patterns of glaucomatous defects in SAP visual fields and successfully separated patients with glaucoma from unaffected controls.

    “Our studies show that this method is able to discriminate between glaucomatous and healthy individuals and can automatically find statistically different binocular visual field patterns in glaucoma patients,” said Dr. Bowd, senior research scientist, director of the Hamilton Glaucoma Center-based Visual Field Assessment Center and co-director of the Hamilton Glaucoma Center-based Imaging Data Evaluation and Analysis Center, Department of Ophthalmology, University of California, San Diego.

    “Patterns identified by unsupervised classifiers are more objectively determined than those observed and described by experts, so they are less biased by previous experience and rules of thumb,” he said.

    Cheryl Guttman Krader
    Cheryl Guttman Krader is a contributor to Dermatology Times, Ophthalmology Times, and Urology Times.

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