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Related Experiment Video

Updated: Feb 20, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

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Published on: April 11, 2025

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Detecting impaired vision caused by AMD from gaze data.

Huiying Liu, Yanwu Xu, Damon Wong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    This study introduces a novel method using gaze data to detect vision impairment from Age-Related Macular Degeneration (AMD). The approach offers advantages over traditional tests, proving effective in identifying AMD-related vision loss.

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    Area of Science:

    • Ophthalmology
    • Medical Technology
    • Data Science

    Background:

    • Age-Related Macular Degeneration (AMD) is a leading cause of blindness in the elderly, resulting in central vision loss due to photoreceptor damage.
    • Current diagnostic methods for AMD-induced vision impairment include Amsler grid, Microperimetry, and Preferential Hyperacuity Perimetry, which have limitations.

    Purpose of the Study:

    • To propose and validate a new method for detecting vision impairment caused by Age-Related Macular Degeneration (AMD) using gaze data.
    • To develop a non-invasive, easily operable test for AMD vision impairment detection.

    Main Methods:

    • Gaze data was collected during fixation and smooth pursuit tasks from patients diagnosed with AMD.
    • Features describing gaze properties were extracted, and a Support Vector Machine (SVM) with a linear kernel was trained to classify AMD-impaired vision.
    • The gold standard Nidek Microperimetry was used for comparison, with data from 74 eyes (57 AMD, 17 normal).

    Main Results:

    • The proposed method effectively detected vision impairment associated with Age-Related Macular Degeneration (AMD) using gaze data.
    • The SVM model demonstrated accuracy in distinguishing between normal and AMD-impaired vision based on gaze patterns.
    • The study confirmed the effectiveness of gaze analysis for AMD vision assessment.

    Conclusions:

    • Gaze data analysis presents a viable and effective alternative for detecting vision impairment in Age-Related Macular Degeneration (AMD).
    • The proposed method offers practical advantages, including ease of operation and no requirement for fixed fixation or patient reporting.
    • This approach has the potential to improve the accessibility and efficiency of AMD screening and diagnosis.