2 research outputs found
Using AI to measure Parkinson’s disease severity at home
Abstract We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson’s disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0–4, following the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, 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 two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters’ average MAE of 0.83 points. 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
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What Patients Say: Large-Scale Analyses of Replies to the Parkinsons Disease Patient Report of Problems (PD-PROP).
BACKGROUND: Free-text, verbatim replies in the words of people with Parkinsons disease (PD) have the potential to provide unvarnished information about their feelings and experiences. Challenges of processing such data on a large scale are a barrier to analyzing verbatim data collection in large cohorts. OBJECTIVE: To develop a method for curating responses from the Parkinsons Disease Patient Report of Problems (PD-PROP), open-ended questions that asks people with PD to report their most bothersome problems and associated functional consequences. METHODS: Human curation, natural language processing, and machine learning were used to develop an algorithm to convert verbatim responses to classified symptoms. Nine curators including clinicians, people with PD, and a non-clinician PD expert classified a sample of responses as reporting each symptom or not. Responses to the PD-PROP were collected within the Fox Insight cohort study. RESULTS: Approximately 3,500 PD-PROP responses were curated by a human team. Subsequently, approximately 1,500 responses were used in the validation phase; median age of respondents was 67 years, 55% were men and median years since PD diagnosis was 3 years. 168,260 verbatim responses were classified by machine. Accuracy of machine classification was 95% on a held-out test set. 65 symptoms were grouped into 14 domains. The most frequently reported symptoms at first report were tremor (by 46% of respondents), gait and balance problems (>39%), and pain/discomfort (33%). CONCLUSION: A human-in-the-loop method of curation provides both accuracy and efficiency, permitting a clinically useful analysis of large datasets of verbatim reports about the problems that bother PD patients