Detection of mild cognitive impairment based on mouse movement data of trail making test
Mild cognitive impairment (MCI) has 10%–20% prevalence in the population above the age of 65, and a significant portion of these people will go on to develop dementia later in their lives. However, if MCI is detected early, preventative measures can be taken to delay the onset of severe symptoms. Cu...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235291482200257X |
| _version_ | 1811185502777245696 |
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| author | Gergely Hanczár Erika Griechisch Nóra Ovád Olivér Máté Törteli Gábor Tóth Dávid Hanák Balázs Vértes András Horváth Anita Kamondi |
| author_facet | Gergely Hanczár Erika Griechisch Nóra Ovád Olivér Máté Törteli Gábor Tóth Dávid Hanák Balázs Vértes András Horváth Anita Kamondi |
| author_sort | Gergely Hanczár |
| collection | DOAJ |
| description | Mild cognitive impairment (MCI) has 10%–20% prevalence in the population above the age of 65, and a significant portion of these people will go on to develop dementia later in their lives. However, if MCI is detected early, preventative measures can be taken to delay the onset of severe symptoms. Current diagnostic methods for MCI are not suitable for regular wide-scale screening. Advances in machine learning algorithms in combination with digital movement data offer rich possibilities for automated MCI detection. This paper introduces a machine learning model that effectively predicts MCI based on only a few seconds of computer mouse movement. To our knowledge, studies directly comparable to ours have not been done before. On a dataset of 70 participants, we demonstrated 80% accuracy in distinguishing healthy controls from patients with MCI. This gives an opportunity to develop a cost-efficient and easy-to-use screening method that could aid the work of healthcare professionals. |
| first_indexed | 2024-04-11T13:31:36Z |
| format | Article |
| id | doaj.art-e50d901db9f949f2a615166ca4fa712d |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-11T13:31:36Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-e50d901db9f949f2a615166ca4fa712d2022-12-22T04:21:49ZengElsevierInformatics in Medicine Unlocked2352-91482022-01-0135101120Detection of mild cognitive impairment based on mouse movement data of trail making testGergely Hanczár0Erika Griechisch1Nóra Ovád2Olivér Máté Törteli3Gábor Tóth4Dávid Hanák5Balázs Vértes6András Horváth7Anita Kamondi8Cursor Insight Ltd., 20-22 Wenlock Road, N1 7GU, London, United Kingdom; Patient Record, 20-22 Wenlock Road, N1 7GU, London, United KingdomCursor Insight Ltd., 20-22 Wenlock Road, N1 7GU, London, United Kingdom; Patient Record, 20-22 Wenlock Road, N1 7GU, London, United KingdomCursor Insight Ltd., 20-22 Wenlock Road, N1 7GU, London, United KingdomCursor Insight Ltd., 20-22 Wenlock Road, N1 7GU, London, United KingdomPatient Record, 20-22 Wenlock Road, N1 7GU, London, United KingdomCursor Insight Ltd., 20-22 Wenlock Road, N1 7GU, London, United Kingdom; Correspondence to: Mester utca 1 3/18, Budapest, H-1095, Hungary.Precognize, Németvölgyi út 2, 1126, Budapest, HungaryNational Institute of Mental Health, Neurology and Neurosurgery, Amerikai út 57, 1145, Budapest, Hungary; Semmelweis University, Department of Anatomy, Histology and Embryology, Tűzoltó u. 58, 1094, Budapest, HungaryNational Institute of Mental Health, Neurology and Neurosurgery, Amerikai út 57, 1145, Budapest, Hungary; Semmelweis University, Department of Neurology, Balassa u. 6, 1083, Budapest, HungaryMild cognitive impairment (MCI) has 10%–20% prevalence in the population above the age of 65, and a significant portion of these people will go on to develop dementia later in their lives. However, if MCI is detected early, preventative measures can be taken to delay the onset of severe symptoms. Current diagnostic methods for MCI are not suitable for regular wide-scale screening. Advances in machine learning algorithms in combination with digital movement data offer rich possibilities for automated MCI detection. This paper introduces a machine learning model that effectively predicts MCI based on only a few seconds of computer mouse movement. To our knowledge, studies directly comparable to ours have not been done before. On a dataset of 70 participants, we demonstrated 80% accuracy in distinguishing healthy controls from patients with MCI. This gives an opportunity to develop a cost-efficient and easy-to-use screening method that could aid the work of healthcare professionals.http://www.sciencedirect.com/science/article/pii/S235291482200257XMouse movementMild cognitive impairmentEarly detectionMachine learning |
| spellingShingle | Gergely Hanczár Erika Griechisch Nóra Ovád Olivér Máté Törteli Gábor Tóth Dávid Hanák Balázs Vértes András Horváth Anita Kamondi Detection of mild cognitive impairment based on mouse movement data of trail making test Informatics in Medicine Unlocked Mouse movement Mild cognitive impairment Early detection Machine learning |
| title | Detection of mild cognitive impairment based on mouse movement data of trail making test |
| title_full | Detection of mild cognitive impairment based on mouse movement data of trail making test |
| title_fullStr | Detection of mild cognitive impairment based on mouse movement data of trail making test |
| title_full_unstemmed | Detection of mild cognitive impairment based on mouse movement data of trail making test |
| title_short | Detection of mild cognitive impairment based on mouse movement data of trail making test |
| title_sort | detection of mild cognitive impairment based on mouse movement data of trail making test |
| topic | Mouse movement Mild cognitive impairment Early detection Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S235291482200257X |
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