NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting
Objective: 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-bas...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| Online Access: | https://ieeexplore.ieee.org/document/10403837/ |
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| author | Quoc Cuong Ngo Nicole McConnell Mohammod Abdul Motin Barbara Polus Arup Bhattacharya Sanjay Raghav Dinesh Kant Kumar |
| author_facet | Quoc Cuong Ngo Nicole McConnell Mohammod Abdul Motin Barbara Polus Arup Bhattacharya Sanjay Raghav Dinesh Kant Kumar |
| author_sort | Quoc Cuong Ngo |
| collection | DOAJ |
| description | Objective: 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. Methods: 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. Results: 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. Conclusion: 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. |
| format | Article |
| id | doaj-art-000e6b5eefd14c43930c9df984b96661 |
| institution | DOAJ |
| issn | 2168-2372 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-000e6b5eefd14c43930c9df984b966612025-08-20T02:56:55ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722024-01-011229129710.1109/JTEHM.2024.335543210403837NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using HandwritingQuoc Cuong Ngo0https://orcid.org/0000-0002-8071-5342Nicole McConnell1Mohammod Abdul Motin2https://orcid.org/0000-0003-1618-3772Barbara Polus3Arup Bhattacharya4Sanjay Raghav5Dinesh Kant Kumar6https://orcid.org/0000-0003-3602-4023School of Engineering, STEM College, RMIT University, Melbourne, VIC, AustraliaGoulburn Valley Health, Shepparton, VIC, AustraliaSchool of Engineering, STEM College, RMIT University, Melbourne, VIC, AustraliaSchool of Engineering, STEM College, RMIT University, Melbourne, VIC, AustraliaGoulburn Valley Health, Shepparton, VIC, AustraliaDepartment of Neurosciences, Monash Medical Centre, Clayton, VIC, AustraliaSchool of Engineering, STEM College, RMIT University, Melbourne, VIC, AustraliaObjective: 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. Methods: 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. Results: 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. Conclusion: 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.https://ieeexplore.ieee.org/document/10403837/Automated diagnosishandwritingParkinson’s diseasesoftware-based medical devices |
| spellingShingle | Quoc Cuong Ngo Nicole McConnell Mohammod Abdul Motin Barbara Polus Arup Bhattacharya Sanjay Raghav Dinesh Kant Kumar NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting IEEE Journal of Translational Engineering in Health and Medicine Automated diagnosis handwriting Parkinson’s disease software-based medical devices |
| title | NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting |
| title_full | NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting |
| title_fullStr | NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting |
| title_full_unstemmed | NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting |
| title_short | NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting |
| title_sort | neurodiag software for automated diagnosis of parkinson x2019 s disease using handwriting |
| topic | Automated diagnosis handwriting Parkinson’s disease software-based medical devices |
| url | https://ieeexplore.ieee.org/document/10403837/ |
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