Asci Francesco, Saurio Gaetano, Pinola Giulia, Falletti Marco, Zampogna Alessandro, Patera Martina, Fattapposta Francesco, Scardapane Simone, Suppa Antonio
Department of Neurosciences and Sensory Organs, AO San Giovanni-Addolorata, Rome, Italy.
Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.
Mov Disord Clin Pract. 2025 Jul 7. doi: 10.1002/mdc3.70208.
Parkinson's disease (PD) leads to handwriting abnormalities primarily characterized by micrographia. Whether micrographia manifests early in PD, worsens throughout the disease, and lastly responds to L-Dopa is still under scientific debate.
We investigated the onset, progression and L-Dopa responsiveness of micrographia in PD, by applying a non-invasive and cheap tool of artificial intelligence- (AI)-based pen-and-paper handwriting analysis.
Fifty-seven PD undergoing chronic L-Dopa treatment were enrolled, including 30 early-stage (H&Y ≤ 2) and 27 mid-advanced stage (H&Y > 2) patients, alongside 25 age- and sex-matched controls. Participants completed two standardized pen-and-paper handwriting tasks in an ecological scenario. Handwriting samples were examined through clinically-based (ie, perceptual) and AI-based (ie, automatic) procedures. Both consistent (ie, average stroke size) and progressive (ie, sequential changes in stroke size) micrographia were evaluated. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the convolutional neural network (CNN) in classifying handwriting in PD and controls.
Clinically- and AI-based analysis revealed a general reduction in stroke size in PD supporting the concept of parkinsonian micrographia. Compared with perceptual analysis, AI-based analysis clarified that micrographia manifests early during the disease, progressively worsens and poorly responds to L-Dopa. The AI models achieved high accuracy in distinguishing PD patients from controls (91%), and moderate accuracy in differentiating early from mid-advanced PD (77%). Lastly, the AI model was not able to detect patients in OFF and ON states.
AI-based handwriting analysis is a valuable non-invasive and cheap tool for detecting and quantifying micrographia in PD, for telemedicine purposes.
帕金森病（PD）会导致以小字症为主要特征的书写异常。小字症是否在帕金森病早期出现、在疾病过程中恶化，以及最终对左旋多巴是否有反应，仍存在科学争议。
我们通过应用一种基于人工智能（AI）的无创且廉价的纸笔书写分析工具，研究帕金森病中小字症的发病、进展及左旋多巴反应性。
纳入57例接受慢性左旋多巴治疗的帕金森病患者，包括30例早期（H&Y≤2）和27例中晚期（H&Y>2）患者，以及25例年龄和性别匹配的对照。参与者在自然场景中完成两项标准化的纸笔书写任务。通过基于临床（即感知）和基于AI（即自动）的程序检查书写样本。评估了一致性（即平均笔画大小）和进行性（即笔画大小的顺序变化）小字症。采用受试者工作特征（ROC）曲线评估卷积神经网络（CNN）对帕金森病患者和对照的笔迹分类准确性。
基于临床和AI的分析均显示帕金森病患者的笔画大小普遍减小，支持帕金森病小字症的概念。与感知分析相比，基于AI的分析表明小字症在疾病早期出现，逐渐恶化，对左旋多巴反应不佳。AI模型在区分帕金森病患者和对照方面具有较高准确性（91%），在区分早期和中晚期帕金森病方面具有中等准确性（77%）。最后，AI模型无法检测出处于关期和开期的患者。
基于AI的笔迹分析是一种有价值的无创且廉价的工具，可用于远程医疗目的，检测和量化帕金森病中的小字症。
