This paper is in the following e-collection/theme issue:

mHealth for Screening (213) Machine Learning (1465) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) mHealth for Rehabilitation (263) mHealth for Data Collection and Research (955)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26320, first published .
A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study

A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study

A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study

Authors of this article:

Takafumi Koyama1 Author Orcid Image ;   Shusuke Sato2 Author Orcid Image ;   Madoka Toriumi2 Author Orcid Image ;   Takuro Watanabe2 Author Orcid Image ;   Akimoto Nimura3 Author Orcid Image ;   Atsushi Okawa1 Author Orcid Image ;   Yuta Sugiura2 Author Orcid Image ;   Koji Fujita3 Author Orcid Image
Article Authors Cited by (13) Tweetations (54) Metrics

Journals

  1. Niño-Ardila M, Castro-Maldonado J. Design of an accessory dedicated to the manipulation of smartphones measured through myoelectric signals. Journal of Physics: Conference Series 2021;2046(1):012032 View
  2. Koyama T, Matsui R, Yamamoto A, Yamada E, Norose M, Ibara T, Kaburagi H, Nimura A, Sugiura Y, Saito H, Okawa A, Fujita K. High-Dimensional Analysis of Finger Motion and Screening of Cervical Myelopathy With a Noncontact Sensor: Diagnostic Case-Control Study. JMIR Biomedical Engineering 2022;7(2):e41327 View
  3. Wang Q, Chu H, Wang H, Jin Y, Zhao X, Weng C, Lu Z. Ring finger sensory latency difference in the diagnosis and treatment of carpal tunnel syndrome. BMC Neurology 2021;21(1) View
  4. Watanabe T, Koyama T, Yamada E, Nimura A, Fujita K, Sugiura Y. The Accuracy of a Screening System for Carpal Tunnel Syndrome Using Hand Drawing. Journal of Clinical Medicine 2021;10(19):4437 View
  5. Watanabe T, Xia C, Fujita K, Sugiura Y. Screening for Carpal Tunnel Syndrome Using Daily Behavior on Mobile Devices. Computer 2023;56(9):62 View
  6. Li X, Wang H, Xu Z, Lu Z, Zhang W, Wang Y, Wang J, Zang F, Yuan W, Chen H, Wu X. A Pilot Study of a Finger Kinematic Parameter-Based Tool for Evaluating Degenerative Cervical Myelopathy. Spine 2023 View
  7. Yener Y, Darol E, Uzun S, Alemdar M, Çolak T. Application of Machine Learning Algorithms for Carpal Tunnel Syndrome. Journal of the Anatomical Society of India 2024;73(2):178 View
  8. Fu Y, Zhang Y, Ye B, Babineau J, Zhao Y, Gao Z, Mihailidis A. Smartphone-Based Hand Function Assessment: Systematic Review. Journal of Medical Internet Research 2024;26:e51564 View
  9. Dababneh S, Colivas J, Dababneh N, Efanov J. Artificial intelligence as an adjunctive tool in hand and wrist surgery: a review. Artificial Intelligence Surgery 2024;4(3):214 View
  10. Gautam N, Gupta S, Shrivastava A. Leveraging wearable sensors and machine learning for posture-based detection of carpal tunnel syndrome. Engineering Research Express 2024;6(4):045213 View

Books/Policy Documents

  1. Niwa M, Hara Y. Mobile Health (mHealth). View