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Related Experiment Video

Updated: Jul 4, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Sequential Deep Learning to Predict Non-Central to Central Geographic Atrophy Progression from OCT Imaging.

Sadia Siraz, Hindolo Kamanda, Ahammed Sakir Nabil

    Medrxiv : the Preprint Server for Health Sciences
    |July 3, 2026
    PubMed
    Summary
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    Deep learning models accurately predict geographic atrophy (GA) progression using optical coherence tomography (OCT) scans over 2-6 years. This enables automated risk stratification for personalized treatment decisions in patients with GA.

    Area of Science:

    • Ophthalmology
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Geographic atrophy (GA) is a leading cause of vision loss in age-related macular degeneration (AMD).
    • Predicting GA progression is crucial for timely therapeutic interventions.
    • Current prediction methods often lack precision and multi-year forecasting capabilities.

    Purpose of the Study:

    • To develop and validate a temporal deep learning framework for predicting GA progression.
    • Utilize longitudinal optical coherence tomography (OCT) sequences for multi-year GA forecasting.
    • Enable automated, individualized risk stratification for GA management.

    Main Methods:

    • Retrospective analysis of 91 dry AMD patients with 455 OCT volumes.
    • Feature extraction from OCT scans using ResNet and ViT architectures.

    Related Experiment Videos

    Last Updated: Jul 4, 2026

    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
    12:50

    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

    Published on: April 14, 2014

  • Temporal modeling with RNN, LSTM, and Transformer networks for disease trajectory prediction.

    • Main Results:

    • High accuracy (ROC-AUC 0.84-1.00) in predicting GA onset from no GA (NGA) over 2-6 years.
    • Transformer models achieved peak AUC of 0.96 for predicting central GA (CGA) involvement.
    • Longer input sequences and temporal interval encoding enhanced prediction performance and stability.

    Conclusions:

    • Temporal deep learning accurately predicts GA progression from longitudinal OCT data.
    • The framework forecasts disease advancement across clinically relevant 2-6 year horizons.
    • This technology supports automated risk stratification to guide complement inhibitor therapy in GA patients.