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Intelligent-assistant system for scleral spur location.

J E Gómez-Correa, L M Torres-Treviño, E Moragrega-Adame

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    This study introduces an artificial neural network (ANN) system for accurately locating the human eye's scleral spur in ultrasound images. The system achieves over 95% efficiency, aiding in ophthalmic image analysis.

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    Area of Science:

    • Ophthalmology
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Accurate identification of the scleral spur is crucial for diagnosing and managing various ocular conditions.
    • Ultrasound biomicroscopy (UBM) provides detailed cross-sectional images of the anterior segment of the eye.
    • Automating the scleral spur localization can improve efficiency and consistency in ophthalmic image analysis.

    Purpose of the Study:

    • To develop and validate a novel system utilizing artificial neural networks (ANNs) for precise scleral spur localization in ultrasound biomicroscopy images.
    • To establish a reliable method for quantifying the relationship between ocular landmarks and scleral spur coordinates.

    Main Methods:

    • A system employing two artificial neural networks (ANNs) was designed to determine scleral spur coordinates.
    • The ANNs learned the relationship between four manually placed landmarks and the scleral spur's position.
    • Expert-defined coordinates from a subject matter specialist served as the ground truth for training.

    Main Results:

    • The trained ANNs demonstrated high accuracy in locating the scleral spur.
    • The developed system achieved an efficiency performance exceeding 95% based on statistical indicators.
    • The ANN-based approach successfully integrated into a software system for practical application.

    Conclusions:

    • The proposed ANN system offers a highly efficient and accurate method for scleral spur localization in ultrasound biomicroscopy.
    • This automated approach has the potential to enhance diagnostic capabilities and streamline ophthalmic image analysis workflows.
    • The system's performance indicates its viability for clinical and research applications in ophthalmology.