Li Qinjie, Yan Jiaxin, Ye Jianfeng, Lv Hao, Zhang Xiaochen, Tu Zhilan, Li Yunxia, Guo Qihao
Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China.
Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Aging Clin Exp Res. 2024 Dec 27;37(1):9. doi: 10.1007/s40520-024-02882-9.
Eye-movement can reflect cognition and provide information on the neurodegeneration, such as Alzheimer's disease (AD). The high cost and limited accessibility of eye-movement recordings have hindered their use in clinics.
We aim to develop an AI-driven eye-tracking tool for assessing AD using mobile devices with embedded cameras.
166 AD patients and 107 normal controls (NC) were enrolled. The subjects completed eye-movement tasks on a pad. We compared the demographics and clinical features of two groups. The eye-movement features were selected using least absolute shrinkage and selection operator (LASSO). Logistic regression (LR) model was trained to classify AD and NC, and its performance was evaluated. A nomogram was established to predict AD.
In training set, the model showed a good area under curve (AUC) of 0.85 for identifying AD from NC, with a sensitivity of 71%, specificity of 84%, positive predictive value of 0.87, and negative predictive value of 0.65. The validation of the model also yielded a favorable discriminatory ability with the AUC of 0.91, sensitivity, specificity, positive predictive value, and negative predictive value of 82%, 91%, 0.93, and 0.77 to identify AD patients from NC.
This novel AI-driven eye-tracking technology has the potential to reliably identify differences in eye-movement abnormalities in AD. The model shows excellent diagnostic performance in identifying AD based on the current data collected. The use of mobile devices makes it accessible for AD patients to complete tasks in primary clinical settings or follow up at home.
眼动可以反映认知情况，并提供有关神经退行性疾病（如阿尔茨海默病（AD））的信息。眼动记录的高成本和有限的可及性阻碍了其在临床中的应用。
我们旨在开发一种由人工智能驱动的眼动追踪工具，用于使用带有嵌入式摄像头的移动设备评估AD。
招募了166例AD患者和107例正常对照（NC）。受试者在平板电脑上完成眼动任务。我们比较了两组的人口统计学和临床特征。使用最小绝对收缩和选择算子（LASSO）选择眼动特征。训练逻辑回归（LR）模型对AD和NC进行分类，并评估其性能。建立列线图以预测AD。
在训练集中，该模型在从NC中识别AD方面显示出良好的曲线下面积（AUC）为0.85，灵敏度为71%，特异性为84%，阳性预测值为0.87，阴性预测值为0.65。该模型的验证也产生了良好的区分能力，AUC为0.91，从NC中识别AD患者的灵敏度、特异性、阳性预测值和阴性预测值分别为82%、91%、0.93和0.77。
这种新型的由人工智能驱动的眼动追踪技术有可能可靠地识别AD患者眼动异常的差异。基于目前收集的数据，该模型在识别AD方面显示出优异的诊断性能。移动设备的使用使AD患者能够在基层临床环境中完成任务或在家中进行随访。
