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    This study introduces RAPDNet, an automated system for detecting relative afferent pupillary defect (RAPD). RAPDNet offers reliable measurements, overcoming limitations of subjective clinical assessments for early optic nerve disorder diagnosis.

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    Area of Science:

    • Ophthalmology
    • Medical Diagnostics
    • Computational Vision

    Background:

    • Pupillary light reflex abnormalities can signal optic nerve disorders, potentially causing irreversible vision loss.
    • Relative afferent pupillary defect (RAPD) assessment currently relies on subjective methods, leading to measurement unreliability.
    • Early diagnosis of optic nerve disorders is crucial to prevent permanent visual impairment.

    Purpose of the Study:

    • To develop and validate an automated framework for objective and reliable RAPD detection.
    • To eliminate subjectivity inherent in current clinical RAPD assessment practices.
    • To improve early diagnosis of conditions affecting the optic nerve.

    Main Methods:

    • Development of an automated framework using a lab-on-a-headset device for pupillary light reflex testing.
    • Implementation of a transfer learning approach, adapting a deep learning object recognition model for pupil detection (RAPDNet).
    • Benchmarking of RAPDNet against handcrafted algorithms using a clinical study dataset.

    Main Results:

    • The automated RAPDNet achieved 90.6% sensitivity and specificity across 64 test cases.
    • Receiver Operating Characteristic (ROC) analysis demonstrated an Area Under the Curve (AUC) of 0.929.
    • RAPDNet outperformed three handcrafted algorithms across nine performance metrics in benchmark tests.

    Conclusions:

    • The proposed automated framework, RAPDNet, provides a reliable and objective method for RAPD detection.
    • This technology has the potential to significantly improve the early diagnosis of optic nerve disorders.
    • Automated pupillary light reflex testing offers a promising advancement in ophthalmic diagnostics.