Jiang Maosong, Liu Yanzhi, Cao Yanlu, Xia Shufeng, Teng Fei, Zhao Wenzhi, Lin Yongzhong, Liu Wenlong
School of Information and Communication Engineering, Dalian University of Technology, No. 2 Linggong Road, 116024, Dalian, China.
Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, 116024, Dalian, China.
J Transl Med. 2025 Jan 14;23(1):65. doi: 10.1186/s12967-024-06044-3.
Parkinson's Disease (PD) is a neurodegenerative disorder, and eye movement abnormalities are a significant symptom of its diagnosis. In this paper, we developed a multi-task driven by eye movement in a virtual reality (VR) environment to elicit PD-specific eye movement abnormalities. The abnormal features were subsequently modeled by using the proposed deep learning algorithm to achieve an auxiliary diagnosis of PD.
We recruited 114 PD patients and 125 healthy controls and collected their eye-tracking data in a VR environment. Participants completed a series of specific VR tasks, including gaze stability, pro-saccades, anti-saccades, and smooth pursuit. After the tasks, eye movement features were extracted from the behaviors of fixations, saccades, and smooth pursuit to establish a PD diagnostic model.
The performance of the models was evaluated through cross-validation, revealing a recall of 97.65%, an accuracy of 92.73%, and a receiver operator characteristic area under the curve (ROC-AUC) of 97.08% for the proposed model.
We extracted PD-specific eye movement features from the behaviors of fixations, saccades, and smooth pursuit in a VR environment to create a model with high accuracy and recall for PD diagnosis. Our method provides physicians with a new auxiliary tool to improve the prognosis and quality of life of PD patients.
帕金森病（PD）是一种神经退行性疾病，眼球运动异常是其诊断的重要症状。在本文中，我们开发了一种在虚拟现实（VR）环境中由眼球运动驱动的多任务，以引发帕金森病特有的眼球运动异常。随后使用所提出的深度学习算法对异常特征进行建模，以实现帕金森病的辅助诊断。
我们招募了114名帕金森病患者和125名健康对照者，并在VR环境中收集他们的眼动数据。参与者完成了一系列特定的VR任务，包括注视稳定性、同向扫视、反向扫视和平滑跟踪。任务完成后，从注视、扫视和平滑跟踪行为中提取眼球运动特征，以建立帕金森病诊断模型。
通过交叉验证对模型的性能进行评估，结果显示所提出模型的召回率为97.65%，准确率为92.73%，曲线下面积（ROC-AUC）为97.08%。
我们从VR环境中的注视、扫视和平滑跟踪行为中提取了帕金森病特有的眼球运动特征，以创建一个用于帕金森病诊断的具有高准确率和召回率的模型。我们的方法为医生提供了一种新的辅助工具，以改善帕金森病患者的预后和生活质量。
