Department of Health Sciences, The Graduate School of Dong-A University, Busan, Republic of Korea.
Biomechanics Laboratory, Dong-A University, Busan, Republic of Korea.
Sci Rep. 2024 Sep 30;14(1):22648. doi: 10.1038/s41598-024-72648-w.
The heterogeneity of Parkinson's disease (PD) presents considerable challenges for accurate diagnosis, particularly during early-stage disease, when the symptoms may be extremely subtle. This study aimed to assess the accuracy of a convolutional neural network (CNN) technique based on the 6-min walk test (6MWT) measured using wearable sensors to distinguish patients with early-stage PD (n = 78) from healthy controls (n = 50). The participants wore six sensors, and performed the 6MWT. The time-series data were converted into new images. The results revealed that the gyroscopic vertical component of the lumbar spine displayed the highest classification accuracy of 83.5%, followed by those of the thoracic spine (83.1%) and right thigh (79.5%) segment. These findings suggest that the 6MWT and CNN models may facilitate earlier diagnosis and monitoring of PD symptoms, enabling clinicians to provide timely treatment during the critical transition from normal to pathologic gait patterns.
帕金森病（PD）的异质性给准确诊断带来了相当大的挑战，尤其是在疾病早期阶段，此时症状可能非常微妙。本研究旨在评估基于可穿戴传感器测量的 6 分钟步行试验（6MWT）的卷积神经网络（CNN）技术区分早期 PD 患者（n=78）和健康对照组（n=50）的准确性。参与者佩戴了六个传感器并进行了 6MWT。时间序列数据被转换为新的图像。结果表明，腰椎的陀螺仪垂直分量显示出最高的分类准确性（83.5%），其次是胸椎（83.1%）和右大腿（79.5%）。这些发现表明，6MWT 和 CNN 模型可能有助于更早地诊断和监测 PD 症状，使临床医生能够在从正常到病理步态模式的关键转变期间提供及时的治疗。
