School of Engineering, STEM CollegeRMIT University Melbourne VIC 3000 Australia.
Goulburn Valley Health Shepparton VIC 3630 Australia.
IEEE J Transl Eng Health Med. 2024 Jan 18;12:291-297. doi: 10.1109/JTEHM.2024.3355432. eCollection 2024.
A change in handwriting is an early sign of Parkinson's disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls.
Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer.
The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD.
In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement - This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson's disease using automated handwriting analysis software, NeuroDiag.
笔迹的变化是帕金森病（PD）的早期迹象。然而，笔迹中存在显著的个体差异，这使得识别病理性笔迹变得困难，尤其是在早期阶段。本文报告了一种基于软件的医疗设备 NeuroDiag，用于通过笔迹模式自动检测帕金森病。NeuroDiag 旨在指导用户执行六项绘图和书写任务，然后将记录上传到服务器进行分析。提取运动学信息和笔迹的笔压并用作基线参数。NeuroDiag 是基于 26 名处于疾病早期的 PD 患者和 26 名匹配对照进行训练的。
本研究招募了 23 名处于疾病早期的帕金森病患者（PPD）、25 名年龄匹配的健康对照者（AMC）和 7 名年轻健康对照者。在顾问神经病学家或其护士的监督下，参与者使用 NeuroDiag。报告实时生成并由独立观察员制表。
参与者无需帮助即可使用 NeuroDiag。手写数据成功上传到服务器，报告实时自动生成。PPD 和 AMC 之间的书写速度存在显著差异（P<0.001）。NeuroDiag 在区分 PPD 和无 PD 患者方面的灵敏度为 86.96%，特异性为 76.92%。
在这项工作中，我们测试了 NeuroDiag 在实时应用中区分 PPD 和 AMC 的可靠性。结果表明，NeuroDiag 有可能用于协助神经病学家和进行远程医疗应用。临床和转化影响声明 - 这项临床前研究表明，使用自动化笔迹分析软件 NeuroDiag 开发针对帕金森病的社区范围筛查计划是可行的。
