Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test
Abstract 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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-09-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-72648-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850203605171175424 |
|---|---|
| author | Hyejin Choi Changhong Youm Hwayoung Park Bohyun Kim Juseon Hwang Sang-Myung Cheon Sungtae Shin |
| author_facet | Hyejin Choi Changhong Youm Hwayoung Park Bohyun Kim Juseon Hwang Sang-Myung Cheon Sungtae Shin |
| author_sort | Hyejin Choi |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-f6dd2f264e74431999bed55ea8bfac2a |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f6dd2f264e74431999bed55ea8bfac2a2025-08-20T02:11:28ZengNature PortfolioScientific Reports2045-23222024-09-0114111510.1038/s41598-024-72648-wConvolutional neural network based detection of early stage Parkinson’s disease using the six minute walk testHyejin Choi0Changhong Youm1Hwayoung Park2Bohyun Kim3Juseon Hwang4Sang-Myung Cheon5Sungtae Shin6Department of Health Sciences, The Graduate School of Dong-A UniversityDepartment of Health Sciences, The Graduate School of Dong-A UniversityBiomechanics Laboratory, Dong-A UniversityDepartment of Health Sciences, The Graduate School of Dong-A UniversityDepartment of Health Sciences, The Graduate School of Dong-A UniversityDepartment of Neurology, School of Medicine, Dong-A UniversityDepartment of Mechanical Engineering, College of Engineering, Dong-A UniversityAbstract 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.https://doi.org/10.1038/s41598-024-72648-wParkinson’s diseaseDetectionArtificial intelligenceDeep learningConvolutional neural networkSix-minute walk test |
| spellingShingle | Hyejin Choi Changhong Youm Hwayoung Park Bohyun Kim Juseon Hwang Sang-Myung Cheon Sungtae Shin Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test Scientific Reports Parkinson’s disease Detection Artificial intelligence Deep learning Convolutional neural network Six-minute walk test |
| title | Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test |
| title_full | Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test |
| title_fullStr | Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test |
| title_full_unstemmed | Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test |
| title_short | Convolutional neural network based detection of early stage Parkinson’s disease using the six minute walk test |
| title_sort | convolutional neural network based detection of early stage parkinson s disease using the six minute walk test |
| topic | Parkinson’s disease Detection Artificial intelligence Deep learning Convolutional neural network Six-minute walk test |
| url | https://doi.org/10.1038/s41598-024-72648-w |
| work_keys_str_mv | AT hyejinchoi convolutionalneuralnetworkbaseddetectionofearlystageparkinsonsdiseaseusingthesixminutewalktest AT changhongyoum convolutionalneuralnetworkbaseddetectionofearlystageparkinsonsdiseaseusingthesixminutewalktest AT hwayoungpark convolutionalneuralnetworkbaseddetectionofearlystageparkinsonsdiseaseusingthesixminutewalktest AT bohyunkim convolutionalneuralnetworkbaseddetectionofearlystageparkinsonsdiseaseusingthesixminutewalktest AT juseonhwang convolutionalneuralnetworkbaseddetectionofearlystageparkinsonsdiseaseusingthesixminutewalktest AT sangmyungcheon convolutionalneuralnetworkbaseddetectionofearlystageparkinsonsdiseaseusingthesixminutewalktest AT sungtaeshin convolutionalneuralnetworkbaseddetectionofearlystageparkinsonsdiseaseusingthesixminutewalktest |