Yang Ni, Liu Jing, Sun Dan, Ding Jiajun, Sun Lingzhi, Qi Xianghua, Yan Wei
Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China.
College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Front Aging Neurosci. 2025 Jul 18;17:1602426. doi: 10.3389/fnagi.2025.1602426. eCollection 2025.
Parkinson's disease is a prevalent neurodegenerative disorder, where early diagnosis is essential for slowing disease progression and optimizing treatment strategies. The latest developments in artificial intelligence (AI) have introduced new opportunities for early detection. Studies have demonstrated that before obvious motor symptoms appear, PD patients exhibit a range of subtle but quantifiable motor abnormalities. This article provides an overview of AI-driven early detection approaches based on various motor symptoms of PD, including eye movement, facial expression, speech, handwriting, finger tapping, and gait. Specifically, we summarized the characteristic manifestations of these motor symptoms, analyzed the features of the data currently collected for AI-assisted diagnosis, collected the publicly available datasets, evaluated the performance of existing diagnostic models, and discussed their limitations. By scrutinizing the existing research methodologies, this review summarizes the application progress of motor symptom-based AI technology in the early detection of PD, explores the key challenges from experimental techniques to clinical translation applications, and proposes future research directions to promote the clinical practice of AI technology in PD diagnosis.
帕金森病是一种常见的神经退行性疾病，早期诊断对于减缓疾病进展和优化治疗策略至关重要。人工智能（AI）的最新发展为早期检测带来了新机遇。研究表明，在明显的运动症状出现之前，帕金森病患者就表现出一系列细微但可量化的运动异常。本文概述了基于帕金森病各种运动症状的人工智能驱动的早期检测方法，包括眼球运动、面部表情、言语、笔迹、手指敲击和步态。具体而言，我们总结了这些运动症状的特征表现，分析了当前为人工智能辅助诊断所收集数据的特点，收集了公开可用的数据集，评估了现有诊断模型的性能，并讨论了它们的局限性。通过审视现有的研究方法，本综述总结了基于运动症状的人工智能技术在帕金森病早期检测中的应用进展，探讨了从实验技术到临床转化应用的关键挑战，并提出了未来的研究方向，以促进人工智能技术在帕金森病诊断中的临床实践。
