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

TL;DR

This paper introduces deep learning models that analyze OCT scans to predict glaucoma progression by capturing structural eye changes, addressing challenges like data noise and imbalance, and outperforming existing methods.

Contribution

The study develops novel semi-supervised CNN-LSTM algorithms for glaucoma progression prediction using OCT data, effectively handling noisy labels and data imbalance.

Findings

Models outperform conventional techniques

Effective handling of noisy and imbalanced data

Improved accuracy in glaucoma progression prediction

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.