Documentdetail
ID kaart
oai:arXiv.org:2303.17573
Onderwerp
Computer Science - Machine Learnin... Computer Science - Artificial Inte...Auteur
Islam, Md Saiful Rahman, Wasifur Abdelkader, Abdelrahman Yang, Phillip T. Lee, Sangwu Adams, Jamie L. Schneider, Ruth B. Dorsey, E. Ray Hoque, EhsanCategorie
Computer Science
Jaar
2023
vermelding datum
18-12-2024
Trefwoorden
parkinson disease motor mae mds-updrs neurologists 0Beschrijving
We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD).
Participants performed a motor task (i.e., tapping fingers) in front of a webcam, and data from 250 global participants were rated by three expert neurologists following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).
The neurologists' ratings were highly reliable, with an intra-class correlation coefficient (ICC) of 0.88.
We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings.
Our machine learning model trained on these measures outperformed an MDS-UPDRS certified rater, with a mean absolute error (MAE) of 0.59 compared to the rater's MAE of 0.79.
However, the model performed slightly worse than the expert neurologists (0.53 MAE).
The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.
Islam, Md Saiful,Rahman, Wasifur,Abdelkader, Abdelrahman,Yang, Phillip T.,Lee, Sangwu,Adams, Jamie L.,Schneider, Ruth B.,Dorsey, E. Ray,Hoque, Ehsan, 2023, Using AI to Measure Parkinson's Disease Severity at Home
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