NystagmusNet: Explainable Deep Learning for Photosensitivity Risk Prediction

TL;DR

NystagmusNet is an explainable AI system that predicts photosensitivity risk in Nystagmus patients and offers real-time visual environment adaptations to improve their daily experience.

Contribution

The paper introduces a novel deep learning model with explainability features for personalized photosensitivity risk prediction in Nystagmus patients.

Findings

Achieved 75% validation accuracy on synthetic data.

Integrated SHAP and GradCAM for model interpretability.

Developed a rule-based system for adaptive visual recommendations.

Abstract

Nystagmus patients with photosensitivity face significant daily challenges due to involuntary eye movements exacerbated by environmental brightness conditions. Current assistive solutions are limited to symptomatic treatments without predictive personalization. This paper proposes NystagmusNet, an AI-driven system that predicts high-risk visual environments and recommends real-time visual adaptations. Using a dual-branch convolutional neural network trained on synthetic and augmented datasets, the system estimates a photosensitivity risk score based on environmental brightness and eye movement variance. The model achieves 75% validation accuracy on synthetic data. Explainability techniques including SHAP and GradCAM are integrated to highlight environmental risk zones, improving clinical trust and model interpretability. The system includes a rule-based recommendation engine for adaptive filter suggestions. Future directions include deployment via smart glasses and reinforcement learning for personalized recommendations.