SWAU-Net: Longitudinal Prediction of Geographic Atrophy via Sliding-Window Attention

TL;DR

A new deep learning model called SWAU-Net improves the prediction of geographic atrophy progression in age-related macular degeneration, helping with clinical trials and patient monitoring.

Contribution

SWAU-Net introduces a novel hybrid architecture combining Transformer-based temporal modeling with U-Net spatial modeling, enhanced by consistency priors to improve prediction accuracy in low-data scenarios.

Findings

SWAU-Net achieved a Growth Mask Dice Similarity Coefficient (DSC) of 0.66, outperforming baseline models.

The model's structural constraints prevent overfitting to imaging noise, improving prediction robustness.

The framework supports more efficient clinical trial designs and personalized patient monitoring for geographic atrophy.

Abstract

Age-related macular degeneration (AMD) is the leading cause of central vision loss in aging populations. Geographic atrophy (GA) is the advanced, non-neovascular form of AMD. Predicting the longitudinal progression of GA remains a critical challenge in ophthalmic clinical practice and clinical trial design. Forecasting the trajectory of GA is complicated by highly variable growth rates and the inherent scarcity of long-term, high-quality imaging data. To address these challenges, we introduce the Sliding Window Attention U-Net (SWAU-Net), a hybrid architecture that integrates Transformer-based temporal modeling of GA growth with precise spatial modeling of GA location with a U-Net convolutional neural network (CNN). To ensure generalization in the low-data regime, SWAU-Net embeds explicit temporal and geometric consistency priors via a weight-shared Sliding Window Attention core and feature-level regularization that preserves sparse, high-frequency lesion boundaries across frames. Experimental results demonstrate that these structural constraints prevent the model from overfitting to imaging noise, achieving a Growth Mask Dice Similarity Coefficient (DSC) of 0.66 (representing the spatial overlap between the predicted and ground truth lesion expansion regions), a significant improvement over unregularized Transformer and standard recurrent baseline models. Our framework provides a robust tool for predicting GA lesion trajectories, potentially supporting more efficient clinical trial designs and personalized patient monitoring.

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