The Archimedes spiral is a clinical tool that aids in the diagnosis and monitoring of essential tremor. However, spiral ratings may vary based on experience and training of the rating physician. This study sought to generate an objective standard model for tremor evaluation using convolutional neural networks. One senior movement disorders neurologist (Neurologist 1) with over 30 years of clinical experience used the Bain and Findley Spirography Rating Scale to rate 1653 Archimedes spiral images from 46 essential tremor patients (mild to severe tremor) and 75 control subjects (no to mild tremor). Neurologist 1's labels were used as the reference standard to train the model. After training the model, a randomly selected subset of spiral testing data was re-evaluated by Neurologist 1, by a second senior movement disorders neurologist (Neurologist 2) with over 27 years of clinical experience, and by our model. Cohen's Weighted Kappa 95% confidence intervals were calculated from all rater comparisons to determine if our model performs with the same proficiency as two senior movement disorders neurologists. The Cohen's Weighted Kappa 95% confidence intervals for the agreement between the reference standard scores and Neurologist 1's rerated scores, for the agreement between the reference standard scores and Neurologist 2's scores, and for the agreement between the reference standard scores and our model's scores were 0.93-0.98, 0.86-0.94, and 0.89-0.96, respectively. With overlapping Cohen's Weighted Kappa 95% confidence intervals for all agreement comparisons, we demonstrate that our model evaluates spirals with the same proficiency as two senior movement disorders neurologists.
Keywords: Bain and findley spirography rating scale; Essential tremor; Machine learning.
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