Screening of Parkinson’s Disease Using Geometric Features Extracted from Spiral Drawings

Brain Sciences

Daniel Z. Press¹  Zisheng Shang²  Richard Deng²  Daniel Sul²  Sammer Marzouk³  Jay Chandra³  Soham Bose³  Raymond Lin³  Alexander Chen³  Anushka Bhaskar³  Sreekar Mantena³  Siva Muthupalaniappan³  Dignity Butts³  Irina Tolkova⁴ 

[1] Cognitive Neurology Unit, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA;Global Alliance for Medical Innovation, Cambridge, MA 02138, USA;Harvard College, Harvard University, Cambridge, MA 02138, USA;School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA;

关键词: Parkinson’s Disease;    biomarker;    Archimedean Spiral;    disease screening;    digitized drawing;    machine learning;   

DOI  :  10.3390/brainsci11101297

来源: DOAJ

【 摘 要 】

Conventional means of Parkinson’s Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

【 授权许可】

Unknown