Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models

Ali Mohammadjafari, Maohua Lin, Min Shi

Background/Objectives: Glaucoma is the second-leading cause of irreversible blindness globally. Retinal images such as color fundus photography have been widely used to detect glaucoma. However, little is known about the effectiveness of using raw clinical notes generated by glaucoma specialists in detecting glaucoma. This study aims to investigate the capability of deep learning approaches to detect glaucoma from clinical notes based on a real-world dataset including 10,000 patients. Different popular models are explored to predict the binary glaucomatous status defined from a comprehensive vision function assessment. Methods: We compared multiple deep learning architectures, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and transformer-based models BERT and BioBERT. LSTM exploits temporal feature dependencies within the clinical notes, while CNNs focus on extracting local textual features, and transformer-based models leverage self-attention to capture rich contextual information and feature correlations. We also investigated the group disparities of deep learning for glaucoma detection in various demographic groups. Results: The experimental results indicate that the CNN model achieved an Overall AUC of 0.80, slightly outperforming LSTM by 0.01. Both models showed disparities and biases in performance across different racial groups. However, the CNN showed reduced group disparities compared to LSTM across Asian, Black, and White groups, meaning it has the advantage of achieving more equitable outcomes. Conclusions: This study demonstrates the potential of deep learning models to detect glaucoma from clinical notes and highlights the need for fairness-aware modeling to address health disparities