Review of Visualization Approaches in Deep Learning Models of Glaucoma

Abstract

Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.

Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data

“…The Shapley analysis that was used to interrogate model decision-making may help explain the model performance at different time horizons. The incorporation of this analysis is also helpful for enhancing the explainability of the models, which is important considering that ML models have frequently been criticized for their "black box" nature [36]. Across all time horizons, several features had a moderate-to-large impact on the model predictions, including age, gender, self-reported race, axial length, CCT, IOP, MD, VFI, and PSD.…”

Visualization Techniques to Enhance the Explainability and Usability of Deep Learning Models in Glaucoma

“…Gu et al also discussed the strategies to improve the interpretability of clinical features from tabular data used to train explainable AI models. 6 Among these, Local Interpretable Model-Agnostic Explanations (LIME) 11 provide key feature visualizations for a model's glaucoma classification, increasing medical professionals' trust, and the Submodular Pick Local Interpretable Model-Agnostic Explanation (SP-LIME) 12 explicates predictive results and glaucoma risk factors, facilitating clearer decision-making. Shapley's value from Cooperative Game Theory, used to quantify individual contributions, is another form of explainable AI.…”