A Tablet-Based App for Carpal Tunnel Syndrome Screening: Diagnostic Case-Control Study

• Published in: JMIR mHealth and uHealth Vol. 7; no. 9; p. e14172
• Main Authors: Fujita, Koji, Watanabe, Takuro, Kuroiwa, Tomoyuki, Sasaki, Toru, Nimura, Akimoto, Sugiura, Yuta
• Format: Journal Article
• Language: English
• Published: Toronto JMIR Publications 13.09.2019
• Subjects: Arthritis; Atrophy; Carpal tunnel syndrome; Classification; Fingers & toes; Hands; Original Paper; Osteoarthritis; Patients; Support vector machines; Surgery; Velocity
• ISSN: 2291-5222; 2291-5222
• DOI: 10.2196/14172

Background Carpal tunnel syndrome (CTS), the most common neuropathy, is caused by a compression of the 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 screening system without medical examination, we have developed a tablet-based CTS detection system, focusing on movement of the thumb in CTS patients; we examined the accuracy of this screening system. Methods A total of 22 female CTS patients, involving 29 hands, and 11 female non-CTS participants were recruited. The diagnosis of CTS was made by hand surgeons based on electrophysiological testing. We developed an iPad-based app that recorded the speed and timing of thumb movements while playing a short game. A support vector machine (SVM) learning algorithm was then used by comparing the thumb movements in each direction among CTS and non-CTS groups with leave-one-out cross-validation; with this, we conducted screening for CTS in real time. Results The maximum speed of thumb movements 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 and P=.005, respectively). The CTS group also took a 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 of 29 CTS hands (93%) were classified as having CTS, while 2 of 29 CTS hands (7%) did not have CTS. CTS and non-CTS were classified with 93% sensitivity and 73% specificity. Conclusions Our newly developed app could classify 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; this may also lead to medical cost-savings.