Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: May 28, 2019
Open Peer Review Period: May 28, 2019 - Jun 4, 2019
Date Accepted: Jul 19, 2019
(closed for review but you can still tweet)
Tablet-based app development for diagnosing carpal tunnel syndrome
ABSTRACT
Background:
Carpal tunnel syndrome (CTS), the most common neuropathy, is caused by a compression of median nerve in the carpal tunnel and is related to aging. The initial symptom is numbness and pain of the median nerve, distributed in the hand area, while thenar muscle atrophy occurs in advanced stages. This atrophy causes failure of thumb motion and results in clumsiness; even after surgery, thenar atrophy does not recover for an extended period. Medical examination and electrophysiological testing are useful to diagnose CTS; however, visits to the doctor tend to be delayed because patients neglect the symptom of numbness in the hand. To avoid thenar atrophy-related clumsiness, early detection of CTS is important.
Objective:
To establish a CTS diagnosis system without medical examination, we have developed a tablet-based CTS detection system, focusing on movement of the thumb in CTS patients and examined the accuracy of this diagnostic system.
Methods:
Twenty-two CTS female patients, involving 29 hands, and 11 non-CTS female participants were recruited. The diagnosis of CTS was made by hand-surgeons, based on electrophysiological testing. We developed an iPad-based app, which recorded the speed and timing of thumb movements while playing a short game. A machine learning algorithm (support vector machine, SVM) was then used, by comparing CTS and non-CTS groups in each direction of thumb movement with leave-one-out cross-validation, to diagnose CTS in real time.
Results:
The maximum speed of thumb movement between CTS and non-CTS groups in each direction did not show any statistically significant difference. The CTS group showed significantly slower average thumb movement speed in the 3 and 6 o’clock directions (P = .03, .005, respectively), and also took significantly longer time to reach the points in the 2, 3, 4, 5, 6, 8, 9, and 11 o’clock directions (P < .05). Cross-validation revealed that 27 CTS patients were diagnosed as having CTS, while 2 CTS patients did not have CTS. CTS and non-CTS individuals were classified correctly in 73% and 93% of cases, respectively.
Conclusions:
Our newly developed app could diagnose disturbance of thumb opposition movement and could be useful as a screening test for CTS patients. Outside of the clinic, this app might be able to detect middle to severe stage CTS and prompt these patients to visit a hand surgery specialist and may lead to medical cost-savings. Clinical Trial: yes
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