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Updated: May 5, 2026

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
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Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes.

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    Summary
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    Predicting neovascular age-related macular degeneration (nAMD) conversion is difficult. A new deep learning method, Morph-SSL, uses unlabeled OCT scans to forecast nAMD risk, enabling earlier treatment.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Predicting conversion from intermediate to neovascular age-related macular degeneration (nAMD) lacks reliable biomarkers.
    • Longitudinal Optical Coherence Tomography (OCT) scans are abundant but underutilized due to limited manual labeling for supervised deep learning (DL).

    Purpose of the Study:

    • To develop a DL model for predicting future nAMD conversion risk using OCT scans.
    • To introduce Morph-SSL, a novel self-supervised learning (SSL) method for longitudinal OCT data.

    Main Methods:

    • Morph-SSL uses pairs of unlabeled OCT scans from different visits, predicting transformations to morph scans.
    • A decoder predicts morphing transformations, enabling intermediate scan generation via linear interpolation.
    • SSL-trained features are fed into a supervised classifier to model time-to-conversion probability.

    Main Results:

    • Morph-SSL trained on 3570 visits from 399 eyes.
    • The classifier achieved an Area Under the Curve (AUC) of 0.779 in predicting nAMD conversion within 6 months.
    • Morph-SSL outperformed end-to-end and other SSL pre-training methods.

    Conclusions:

    • Automated prediction of nAMD onset risk is feasible with DL and SSL.
    • This approach can facilitate timely treatment and personalized AMD management.