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    A novel deep learning method, PRE U-net, accurately detects the fovea centralis in optical coherence tomography (OCT) scans. This automated approach aids in diagnosing macular diseases and guiding treatment for better visual function assessment.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • The fovea centralis is crucial for sharp central vision, and its precise localization in optical coherence tomography (OCT) volumes is vital for clinical assessments.
    • Accurate localization aids in evaluating visual function and guiding treatment strategies for various macular diseases.

    Purpose of the Study:

    • To introduce a novel, fully automated deep learning approach called "PRE U-net" for precise fovea centralis detection.
    • To address the challenge of fovea localization as a pixel-wise regression task using OCT image volumes.

    Main Methods:

    • The PRE U-net deep learning model was developed, utilizing 2D B-scans sampled from OCT volumes with integrated spatial location information.
    • The model was trained, validated, and tested on a large dataset comprising 5586 OCT volumes from 1541 eyes.

    Main Results:

    • The PRE U-net demonstrated superior performance compared to existing state-of-the-art methods for automated fovea centralis localization.
    • The method showed improved robustness in localization across diverse patient groups, including healthy individuals and those with neovascular age-related macular degeneration (nAMD), diabetic macula edema (DME), and retinal vein occlusion (RVO).

    Conclusions:

    • The PRE U-net offers a highly effective and robust automated solution for fovea centralis detection in OCT imaging.
    • This advancement holds significant value for clinical practice, enhancing the diagnosis and management of sight-threatening macular diseases.