用AI预测光敏感患者高风险环境并实时推荐防护方案
NystagmusNet: Explainable Deep Learning for Photosensitivity Risk Prediction
- 双分支网络结合合成数据，评估光照与眼动波动的光敏感风险
- 在合成数据上验证准确率达75%，可识别高风险光照区域
- 通过SHAP和GradCAM提升可解释性，适合临床医生与患者使用
光敏感性眼震患者因环境亮度引发不自主眼动而面临重大日常挑战。现有辅助手段仅限于症状缓解，缺乏个性化预测。本文提出NystagmusNet，一种基于AI的系统，可预测高风险视觉环境并实时推荐视觉适应策略。该系统采用双分支卷积神经网络，在合成与增强数据集上训练，根据环境亮度和眼动方差估计光敏感风险评分。模型在合成数据上的验证准确率达75%。集成SHAP与GradCAM等可解释性技术，突出显示环境风险区域，提升临床信任度与模型透明度。系统还包含基于规则的推荐引擎，提供自适应滤镜建议。未来将探索通过智能眼镜部署，并引入强化学习实现个性化推荐。
原文摘要 · Abstract (English)
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.
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