Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s disease
Abstract In this uncontrolled, open-label exploratory clinical study, the authors explore the potential of blink data as a digital biomarker for estimating clinical indices of Parkinson’s disease (PD) using a machine learning approach. Blink data were collected from 20 patients with PD before and af...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Parkinson's Disease |
| Online Access: | https://doi.org/10.1038/s41531-025-01094-w |
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| author | Noriko Nishikawa Shin Tejima Daiki Kamiyama Mitsumasa Kurita Koshi Yamamoto Satoki Imai Wataru Sako Genko Oyama Taku Hatano Nobutaka Hattori |
| author_facet | Noriko Nishikawa Shin Tejima Daiki Kamiyama Mitsumasa Kurita Koshi Yamamoto Satoki Imai Wataru Sako Genko Oyama Taku Hatano Nobutaka Hattori |
| author_sort | Noriko Nishikawa |
| collection | DOAJ |
| description | Abstract In this uncontrolled, open-label exploratory clinical study, the authors explore the potential of blink data as a digital biomarker for estimating clinical indices of Parkinson’s disease (PD) using a machine learning approach. Blink data were collected from 20 patients with PD before and after (up to 4 h) L-dopa/decarboxylase inhibitor administration. Concurrent assessments of patient diary-based ON/OFF and dyskinesia, L-dopa plasma concentration, and MDS-UPDRS Part III scores were conducted at 30 min intervals. The models were developed to predict clinical symptoms based on blink data collected at 3 min intervals. The most effective post-processing models accurately predicted the ON/OFF states (mean area under the receiver operating characteristic curve (AUCROC) = 0.87) and the presence of dyskinesia (mean AUCROC = 0.84). They also moderately predicted MDS-UPDRS Part III scores (mean Spearman’s correlation ρ = 0.54) and plasma L-dopa concentrations (ρ = 0.57). Our findings highlight the potential of the spontaneous eye blink as a noninvasive, real-time digital biomarker for PD. |
| format | Article |
| id | doaj-art-0ef1e20f64ae4330be9887f979e0a834 |
| institution | Kabale University |
| issn | 2373-8057 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Parkinson's Disease |
| spelling | doaj-art-0ef1e20f64ae4330be9887f979e0a8342025-08-24T11:14:35ZengNature Portfolionpj Parkinson's Disease2373-80572025-08-0111111210.1038/s41531-025-01094-wSpontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s diseaseNoriko Nishikawa0Shin Tejima1Daiki Kamiyama2Mitsumasa Kurita3Koshi Yamamoto4Satoki Imai5Wataru Sako6Genko Oyama7Taku Hatano8Nobutaka Hattori9Department of Neurology, Juntendo University School of MedicineSumitomo Pharma Co., Ltd.Department of Neurology, Juntendo University School of MedicineSumitomo Pharma Co., Ltd.Sumitomo Pharma Co., Ltd.Sumitomo Pharma Co., Ltd.Department of Neurology, Juntendo University School of MedicineDepartment of Neurology, Juntendo University School of MedicineDepartment of Neurology, Juntendo University School of MedicineDepartment of Neurology, Juntendo University School of MedicineAbstract In this uncontrolled, open-label exploratory clinical study, the authors explore the potential of blink data as a digital biomarker for estimating clinical indices of Parkinson’s disease (PD) using a machine learning approach. Blink data were collected from 20 patients with PD before and after (up to 4 h) L-dopa/decarboxylase inhibitor administration. Concurrent assessments of patient diary-based ON/OFF and dyskinesia, L-dopa plasma concentration, and MDS-UPDRS Part III scores were conducted at 30 min intervals. The models were developed to predict clinical symptoms based on blink data collected at 3 min intervals. The most effective post-processing models accurately predicted the ON/OFF states (mean area under the receiver operating characteristic curve (AUCROC) = 0.87) and the presence of dyskinesia (mean AUCROC = 0.84). They also moderately predicted MDS-UPDRS Part III scores (mean Spearman’s correlation ρ = 0.54) and plasma L-dopa concentrations (ρ = 0.57). Our findings highlight the potential of the spontaneous eye blink as a noninvasive, real-time digital biomarker for PD.https://doi.org/10.1038/s41531-025-01094-w |
| spellingShingle | Noriko Nishikawa Shin Tejima Daiki Kamiyama Mitsumasa Kurita Koshi Yamamoto Satoki Imai Wataru Sako Genko Oyama Taku Hatano Nobutaka Hattori Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s disease npj Parkinson's Disease |
| title | Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s disease |
| title_full | Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s disease |
| title_fullStr | Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s disease |
| title_full_unstemmed | Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s disease |
| title_short | Spontaneous eye blink-based machine learning for tracking clinical fluctuations in Parkinson’s disease |
| title_sort | spontaneous eye blink based machine learning for tracking clinical fluctuations in parkinson s disease |
| url | https://doi.org/10.1038/s41531-025-01094-w |
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