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DP-MDLA Net:在帕金森病中检测平滑追踪异常

Zhiyuan Tan¹, Jie Zhao², Liwen Kong¹

¹College of Information Engineering, Dalian University, Dalian, Liaoning, China.

|February 16, 2026

概括

这项研究开发了一个智能手机视频框架,使用深度学习来检测帕金森病 (PD) 中的光滑追踪异常. 该工具准确地量化了这些眼动问题,有助于PD评估.

科学领域:

眼科医生眼科眼科
神经学神经学
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

帕金森病 (PD) 是一种神经退行性疾病,影响运动控制.
顺的眼动异常是PD的早期特征.
当前的评估方法往往需要专门的设备和临床环境.

研究的目的:

开发和验证基于深度学习的框架,用于使用智能手机视频量化平滑追踪异常.
为了使PD相关眼动缺陷的可访问,客观和标准化的评估.

主要方法:

收集了来自18名帕金森病患者的智能手机视频 (N=54个视频).
标注 1767 个事件级样本 (2 秒窗口) 用于平滑追踪事件.
使用的MediaPipe FaceLandmarker用于眼睛地标提取和DP-MDLA Net用于分类.

主要成果:

该框架在测试组中实现了高准确度 (96.59%),精度 (97.50%),回忆 (95.12%) 和AUC (0.9939).
交叉验证和独立于受试者的验证证明了强大的性能 (精度>93%,AUC>0.96).
废弃研究证实了空间规范化对准确性的重要性.

结论:

一个基于智能手机视频的新型框架有效量化了帕金森病中的光滑追踪异常.
这项技术支持便携式的床边评估和PD的纵向监测.
该框架为PD评估提供了一种具有成本效益和可访问的专用设备替代方案.

关键词:

帕金森氏症是帕金森氏症的一种疾病.计算机视觉计算机视觉深度学习是一种深度学习.眼球运动分析分析智能手机的智能手机智能手机的智能手机.顺利的追求顺利的追求

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