GlaLSTM: A Concurrent LSTM Stream Framework for Glaucoma Detection via Biomarker Mining

2024-08-28

AI Summary

To address the challenges of modeling dynamic biomarker interactions and poor model interpretability in early glaucoma detection, this paper proposes a biomarker-driven concurrent LSTM temporal modeling framework. The framework employs multi-stream LSTMs to jointly model the longitudinal evolution of distinct clinical biomarkers—such as retinal nerve fiber layer thickness—and integrates implicit relational learning with feature attribution mechanisms to achieve synergistic optimization of dynamic interaction modeling and interpretability. Unlike conventional CNN-based image analysis methods, our approach overcomes the limitation of lacking mechanistic insight. Evaluated on a multicenter dataset, it achieves a 3.2% accuracy improvement over state-of-the-art models. Moreover, it significantly enhances the visualization and clinical interpretability of key biomarker contributions. This work establishes a new paradigm for precision early glaucoma screening that simultaneously delivers high performance and model transparency.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Glaucoma is a complex group of eye diseases marked by optic nerve damage, commonly linked to elevated intraocular pressure and biomarkers like retinal nerve fiber layer thickness. Understanding how these biomarkers interact is crucial for unraveling glaucoma's underlying mechanisms. In this paper, we propose GlaLSTM, a novel concurrent LSTM stream framework for glaucoma detection, leveraging latent biomarker relationships. Unlike traditional CNN-based models that primarily detect glaucoma from images, GlaLSTM provides deeper interpretability, revealing the key contributing factors and enhancing model transparency. This approach not only improves detection accuracy but also empowers clinicians with actionable insights, facilitating more informed decision-making. Experimental evaluations confirm that GlaLSTM surpasses existing state-of-the-art methods, demonstrating its potential for both advanced biomarker analysis and reliable glaucoma detection.

Problem

• Detects glaucoma via biomarker interaction analysis
• Improves interpretability and transparency in glaucoma diagnosis
• Enhances detection accuracy with actionable clinical insights

Innovation

• Concurrent LSTM streams for biomarker mining
• Deep interpretability via latent relationships
• Superior accuracy and clinical insights