Zhu Zhaohui, Wu E, Leng Pengfei, Sun Jiajun, Ma Mingming, Pan Zhigeng
School of Public Administration, Hangzhou Normal University, Hangzhou, China.
School of Computer Science and Technology, Huaiyin Normal University, Huaian, China.
PLoS One. 2025 Jul 14;20(7):e0327733. doi: 10.1371/journal.pone.0327733. eCollection 2025.
Parkinson's disease (PD), a progressive neurodegenerative disorder prevalent in aging populations, manifests clinically through characteristic motor impairments including bradykinesia, rigidity, and resting tremor. Early detection and timely intervention may delay disease progression. Spiral drawing tasks have been established as effective auxiliary diagnostic tools. This study developed a hybrid deep learning model to analyze motion data from finger drawings of spiral and wave lines on smartphone screens, aiming to detect early Parkinson's disease.
We recruited 58 age-matched participants (28 early idiopathic PD patients: 68.4 ± 5.7 years; 30 healthy controls: 68.0 ± 4.5 years) for two smartphone-based drawing tasks (spiral and wave). A custom-developed app recorded finger touch coordinates, instantaneous movement speed, and timestamps at a sampling frequency of 60 Hz. Our hybrid model combined multi-scale convolutional feature extraction (using parallel 1D-Convolutional branches) with bidirectional temporal pattern recognition (via gated recurrent unit [GRU] networks) to analyze movement abnormalities and detect the disease.
The proposed model demonstrated robust diagnostic performance, achieving a cross-validation accuracy of 87.93% for spiral drawings (89.64% sensitivity, 86.33% specificity). Wave drawings yielded 87.24% accuracy (86.79% sensitivity, 87.67% specificity). The integration of both tasks achieved 91.20% accuracy (95% CI: 89.2%-93.2%) with balanced sensitivity (91.43%) and specificity (91.00%).
This study establishes the technical feasibility of a hybrid deep learning framework for early PD detection using smartphone-captured finger motion dynamics. The developed model effectively combines one-dimensional convolutional neural networks with bidirectional GRUs to analyze drawing tasks. Distinct from existing approaches that rely on clinical rating scales, neuroimaging modalities, or stylus-based digital assessments, this telemedicine-compatible method requires only bare-finger interactions on consumer-grade smartphones and enables operator-independent assessments. Furthermore, it facilitates cost-effective and convenient PD assessment in remote healthcare and patient monitoring, particularly in resource-limited settings.
帕金森病（PD）是一种在老年人群中普遍存在的进行性神经退行性疾病，临床上表现为包括运动迟缓、僵硬和静止性震颤在内的特征性运动障碍。早期检测和及时干预可能会延缓疾病进展。螺旋线绘制任务已被确立为有效的辅助诊断工具。本研究开发了一种混合深度学习模型，用于分析智能手机屏幕上螺旋线和波浪线手指绘图的运动数据，旨在检测早期帕金森病。
我们招募了58名年龄匹配的参与者（28名早期特发性PD患者：68.4±5.7岁；30名健康对照者：68.0±4.5岁）进行两项基于智能手机的绘图任务（螺旋线和波浪线）。一个定制开发的应用程序以60Hz的采样频率记录手指触摸坐标、瞬时移动速度和时间戳。我们的混合模型将多尺度卷积特征提取（使用并行的一维卷积分支）与双向时间模式识别（通过门控循环单元[GRU]网络）相结合，以分析运动异常并检测疾病。
所提出的模型表现出强大的诊断性能，螺旋线绘图的交叉验证准确率达到87.93%（灵敏度89.64%，特异性86.33%）。波浪线绘图的准确率为87.24%（灵敏度86.79%，特异性87.67%）。两项任务的整合实现了91.20%的准确率（95%CI：89.2%-93.2%），灵敏度（91.43%）和特异性（91.00%）保持平衡。
本研究确立了使用智能手机捕获的手指运动动力学的混合深度学习框架用于早期PD检测的技术可行性。所开发的模型有效地将一维卷积神经网络与双向GRU相结合以分析绘图任务。与现有的依赖临床评分量表、神经影像学模式或基于触控笔的数字评估方法不同，这种与远程医疗兼容的方法仅需要在消费级智能手机上进行裸手指交互，并能够实现独立于操作员的评估。此外，它有助于在远程医疗保健和患者监测中进行经济高效且便捷的PD评估，特别是在资源有限的环境中。
