The accuracy of a screening system for carpal tunnel syndrome using hand drawings

Takuro Watanabe, Takafumi Koyama, Eriku Yamada, Akimoto Nimura, Koji Fujita, Yuta Sugiura

Research output: Contribution to journalArticlepeer-review

Abstract

When carpal tunnel syndrome (CTS), an entrapment neuropathy, becomes severe, thumb motion is reduced, which affects manual dexterity, such as causing difficulties in writing; therefore, early detection of CTS by screening is desirable. To develop a screening method for CTS, we developed a tablet app to measure the stylus trajectory and pressure of the stylus tip when drawing a spiral on a tablet screen using a stylus and, subsequently, used these data as training data to predict the classification of participants as non‐CTS or CTS patients using a support vector machine. We recruited 33 patients with CTS and 31 healthy volunteers for this study. From our results, non‐ CTS and CTS were classified by our screening method with 82% sensitivity and 71% specificity. Our CTS screening method can facilitate the screening for potential patients with CTS and provide a quantitative assessment of CTS.

Original languageEnglish
Article number4437
JournalJournal of Clinical Medicine
Volume10
Issue number19
DOIs
Publication statusPublished - 2021 Oct 1

Keywords

  • Carpal tunnel syndrome
  • Drawing
  • Machine learning
  • Manual dexterity
  • Mobility
  • Nerve
  • Pain
  • Screening
  • Support vector machine
  • Tablet app

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint

Dive into the research topics of 'The accuracy of a screening system for carpal tunnel syndrome using hand drawings'. Together they form a unique fingerprint.

Cite this