Browsing by Subject "diagnostics"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Leveraging Machine Learning Tools To Develop Objective, Interpretable, And Accessible Assessments Of Postural Instability In Parkinson'S Disease
    (2023-04) Herbers, Cara
    Parkinson's disease (PD) is the second most common neurodegenerative disease in the United States, affecting 1 million Americans. PD-related postural instability (PI) is one of the most disabling motor symptoms of PD since it is associated with increased falls and loss of independence. PI has little or no response to current PD treatments, the underlying mechanisms are poorly understood, and the current clinical assessments are subjective and introduce human error. There is a need for improved diagnostic tools of PI for clinicians to better characterize, understand, and treat PD-related PI. Several criteria are necessary to address this clinical need: (1) the clinical rating of PI should be quantified objectively, (2) additional postural tasks should be clinically assessed and quantified, and (3) the assessments of PI should occur more frequently than a biannual clinical assessment. This project sought to develop two novel approaches to address these criteria. First, deep learning markerless pose estimation was leveraged to assess reactive step length in response to shoulder pull and surface translation perturbations for individuals with and without PD. Reactive step length was altered in PD (significantly for treadmill perturbations, and with an insignificant trend for shoulder pull perturbations), and improved by dopamine replacement therapy. Next, insole plantar pressure sensor data from 111 subjects (44 PD, 67 controls) were collected and used to assess PD-related PI during typical daily balance tasks. Machine learning models were developed to accurately identify PD from young controls (area under the curve (AUC) 0.99 +/- 0.00), PD from age-matched controls (AUC 0.99 +/- 0.01), and PD non-fallers from PD fallers (AUC 0.91 +/- 0.08). It was seen that utilizing features from both static and active tasks significantly improved classification performances and that all tasks were useful for separating controls from PD; however, tasks with higher postural threat were preferred for separating PD non-fallers from PD fallers. This work produced numerous clinical and translational implications. Notably, (1) simple and accessible quantitative measures can be used to identify PD and individuals with PD who fall, and (2) machine learning models can be leveraged for implementing, quantifying, and interpreting these measures into something clinically useful.