Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye

• URL: http://arxiv.org/abs/2406.05605v1
• Date: Sun, 9 Jun 2024 01:12:41 GMT
• Title: Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye
• Authors: Sayan Mandal,
• Abstract summary: Glaucoma is a chronic eye disease characterized by optic neuropathy, leading to irreversible vision loss. Early detection is crucial to monitor atrophy and develop treatment strategies to prevent further vision impairment. In this study, we use deep learning models to identify complex disease traits and progression criteria.
• Score: 0.20718016474717196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Glaucoma is a chronic eye disease characterized by optic neuropathy, leading to irreversible vision loss. It progresses gradually, often remaining undiagnosed until advanced stages. Early detection is crucial to monitor atrophy and develop treatment strategies to prevent further vision impairment. Data-centric methods have enabled computer-aided algorithms for precise glaucoma diagnosis. In this study, we use deep learning models to identify complex disease traits and progression criteria, detecting subtle changes indicative of glaucoma. We explore the structure-function relationship in glaucoma progression and predict functional impairment from structural eye deterioration. We analyze statistical and machine learning methods, including deep learning techniques with optical coherence tomography (OCT) scans for accurate progression prediction. Addressing challenges like age variability, data imbalances, and noisy labels, we develop novel semi-supervised time-series algorithms: 1. Weakly-Supervised Time-Series Learning: We create a CNN-LSTM model to encode spatiotemporal features from OCT scans. This approach uses age-related progression and positive-unlabeled data to establish robust pseudo-progression criteria, bypassing gold-standard labels. 2. Semi-Supervised Time-Series Learning: Using labels from Guided Progression Analysis (GPA) in a contrastive learning scheme, the CNN-LSTM architecture learns from potentially mislabeled data to improve prediction accuracy. Our methods outperform conventional and state-of-the-art techniques.

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