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

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2024

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Abstract

Glaucoma is a group of chronic eye diseases characterized by optic neuropathy, which causes irreversible vision loss. It is caused by progressive degeneration of the optic nerve, leading to gradual loss of the visual field from the periphery to the center, resulting in blindness if left untreated. Since the changes are gradual and the damage progresses generally slowly, glaucoma development is insidious and often diagnosed until it reaches an advanced stage. Early detection of glaucoma progression is necessary to monitor the atrophy and formulate treatment strategies to halt progressive functional vision impairments. The availability of data centric methods have made it possible for researchers to develop computer-aided algorithms for the clinical diagnosis of glaucoma and capture accurate disease characteristics. In this research, we use deep learning models, one such forefront, to identify complex disease characteristics and progression criteria, enabling the detection of subtle changes indicative of glaucoma progression.

To this end, we investigate the structure-function relationship of glaucoma progression and explore the possibility of predicting functional impairment from structural eye deterioration. We also analyze various statistical and machine-learning methods that have aided previous attempts to estimate progression, including emerging deep-learning techniques that use structural features like optical coherence tomography (OCT) scans to predict glaucoma progression accurately. We show through our investigations that these methods are still prone to confounding risk factors, especially variability due to age, data imbalances, potential noisy labels, lack of gold standard criteria, etc. We developed novel semi-supervised time-series algorithms to overcome these multifaceted challenges using unique data-driven approaches:

Weakly-Supervised Time-Series Learning: We develop a convolutional neural network-long short-term memory (CNN-LSTM) base model to encode the spatiotemporal features from the OCT scan sequence taken over a fixed follow-up. We model the rest of the deep learning architecture on the fact that original OCT sequences exhibit age-related progression, and reshuffling the sequence order, along with the knowledge of healthy eyes from a positive-unlabeled dataset, can establish robust pseudo-progression criteria for glaucoma. This circumvents the need for gold standard labels for disease progression.

Semi-supervised Time-Series Learning: We extend the above notion to a labeled case where labels are obtained from Guided Progression Analysis (GPA), a well-known, stable, and accurate functional assessment for glaucoma progression, but might be prone to noisy labels due to nuances in data acquisition. We model the age-related structural progression as a pseudo-identifier for glaucoma progression. We use this knowledge in a contrastive learning scheme where the foundational CNN-LSTM base learns accurate spatiotemporal characteristics from potentially mislabeled data and improves predictions.

Finally, we compare and show that these methods outperform conventional and state-of-the-art techniques.

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Citation

Mandal, Sayan (2024). Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/30796.

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