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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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GlaLSTM: A Concurrent LSTM Stream Framework for Glaucoma Detection via Biomarker Relationship Mining.

Cheng Huang, Weizheng Xie, Tsengdar Lee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    This study introduces GlaLSTM, a new AI framework for detecting glaucoma by analyzing biomarker interactions. It offers improved accuracy and transparency compared to traditional methods, aiding clinical decisions.

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    Area of Science:

    • Ophthalmology
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Glaucoma is a leading cause of irreversible blindness worldwide.
    • Elevated intraocular pressure and retinal nerve fiber layer thickness are key glaucoma biomarkers.
    • Understanding biomarker interactions is vital for glaucoma mechanisms.

    Purpose of the Study:

    • To propose GlaLSTM, a novel concurrent LSTM stream framework for glaucoma detection.
    • To leverage latent biomarker relationships for improved glaucoma diagnosis.
    • To enhance model interpretability and transparency in glaucoma detection.

    Main Methods:

    • Developed a novel concurrent LSTM stream framework named GlaLSTM.
    • Utilized latent biomarker relationships for glaucoma detection.
    • Compared GlaLSTM with traditional CNN-based models.

    Main Results:

    • GlaLSTM provides deeper interpretability than CNN-based models.
    • The framework reveals key contributing factors to glaucoma.
    • GlaLSTM demonstrates superior performance over state-of-the-art methods.

    Conclusions:

    • GlaLSTM offers advanced biomarker analysis and reliable glaucoma detection.
    • The model enhances transparency and provides actionable clinical insights.
    • This approach facilitates more informed decision-making for clinicians.