Spiral drawing: Quantitative analysis and artificial-intelligence-based diagnosis using a smartphone

J Neurol Sci. 2020 Apr 15:411:116723. doi: 10.1016/j.jns.2020.116723. Epub 2020 Feb 4.


Background: The evaluation of neurological examination in clinical practice still remains qualitative or semi-quantitative, and the results often vary depending on an examiner's skill level and are less objective. In this study, we developed a smartphone-based application to investigate quantifying neurological examinations using hand-drawn spirals and diagnose patients with tremor using artificial intelligence (AI).

Methods: This study included 24 and 26 patients with essential tremor (ET) and cerebellar disease (CD), respectively, and 41 age-matched normal controls (NCs). We obtained 69, 46, and 56 hand-drawn spirals from the NC, ET, and CD groups, respectively, as image data captured by smartphones. The patients traced a printed reference spiral. The length of this spiral was compared with the reference spiral length (% of spiral length) and the total deviation area between these spirals was calculated. The server also estimates the diagnostic probability through AI.

Results: The quantified spiral analysis (% of spiral length and deviation area) significantly correlated with disease severity in each disease group, and significant differences in the deviation area were observed among all groups. The AI diagnosis showed 79%, 70%, and 73% accuracies for the NC, ET, and CD groups, respectively.

Conclusion: This study indicates the possibility of using a smartphone as a medical examination tool and demonstrates the application of AI in neurological examinations.

Keywords: Artificial intelligence; Cerebellar ataxia; Machine learning; Smartphone; Spiral; Tremor.

MeSH terms

  • Artificial Intelligence
  • Essential Tremor*
  • Humans
  • Intelligence
  • Motor Skills
  • Smartphone*