Diabetes is currently one of the major public health threats. The essential components for effective treatment of diabetes include early diagnosis and regular monitoring. However, health-care providers are often short of human resources to closely monitor populations at risk. In this work, a video-based eye-tracking method is proposed as a low-cost alternative for detection of diabetic neuropathy. The method is based on the tracking of the eye-trajectories recorded on videos while the subject follows a target on a screen, forcing saccadic movements. Upon extraction of the eye trajectories, representation of the obtained time-series is made with the help of heteroscedastic ARX (H-ARX) models, which capture the dynamics and latency on the subject's response, while features based on the H-ARX model's predictive ability are subsequently used for classification. The methodology is evaluated on a population constituted by 11 control and 20 insulin-treated diabetic individuals suffering from diverse diabetic complications including neuropathy and retinopathy. Results show significant differences on latency and eye movement precision between the populations of control subjects and diabetics, while simultaneously demonstrating that both groups can be classified with an accuracy of 95%. Although this study is limited by the small sample size, the results align with other findings in the literature and encourage further research.

糖尿病是目前主要的公共卫生威胁之一。有效治疗糖尿病的基本要素包括早期诊断和定期监测。然而，卫生保健提供者往往缺乏密切监测高危人群的人力资源。在这项工作中，提出了一种基于视频的眼动追踪方法作为检测糖尿病神经病变的低成本替代方法。该方法基于跟踪视频中记录的眼动轨迹，同时对象跟随屏幕上的目标，迫使扫视运动。在提取眼球轨迹后，在异方差 ARX (H-ARX) 模型的帮助下对所获得的时间序列进行表示，该模型捕获主体响应的动态和延迟，而基于 H-ARX 模型的特征 s 的预测能力随后用于分类。该方法在由患有多种糖尿病并发症（包括神经病变和视网膜病变）的 11 名对照和 20 名接受胰岛素治疗的糖尿病个体组成的人群中进行评估。结果显示对照组和糖尿病患者群体之间的潜伏期和眼球运动精度存在显着差异，同时表明两组的分类准确率都可以达到 95%。尽管这项研究受到样本量小的限制，但结果与文献中的其他发现一致，并鼓励进一步研究。结果显示对照组和糖尿病患者群体之间的潜伏期和眼球运动精度存在显着差异，同时表明两组的分类准确率都可以达到 95%。尽管这项研究受到样本量小的限制，但结果与文献中的其他发现一致，并鼓励进一步研究。结果显示对照组和糖尿病患者群体之间的潜伏期和眼球运动精度存在显着差异，同时表明两组的分类准确率都可以达到 95%。尽管这项研究受到样本量小的限制，但结果与文献中的其他发现一致，并鼓励进一步研究。