scholarly journals The Accuracy of a Screening System for Carpal Tunnel Syndrome Using Hand Drawing

2021 ◽  
Vol 10 (19) ◽  
pp. 4437
Author(s):  
Takuro Watanabe ◽  
Takafumi Koyama ◽  
Eriku Yamada ◽  
Akimoto Nimura ◽  
Koji Fujita ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Quantitative Assessment ◽  
Screening Method ◽  
Training Data ◽  
Support Vector ◽  
Screening System ◽  
Tunnel Syndrome ◽  
Thumb Motion

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.

The Accuracy of a Screening System for Carpal Tunnel Syndrome Using Hand Drawing: Case-Control Study (Preprint)

2021 ◽  
Author(s):  
Takuro Watanabe ◽  
Takafumi Koyama ◽  
Eriku Yamada ◽  
Akimoto Nimura ◽  
Koji Fujita ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Wrist Joint ◽  
Screening Method ◽  
Ring Finger ◽  
Manual Dexterity ◽  
Training Data ◽  
Support Vector ◽  
Initial Symptoms ◽  
Tunnel Syndrome

BACKGROUND Carpal tunnel syndrome (CTS) is an entrapment neuropathy that occurs due to compression of the median nerve as it passes through the carpal tunnel at the wrist joint. The initial symptoms are numbness and sensory disturbance from the thumb to the ring finger. As CTS becomes severe, thumb motion is reduced, which affects manual dexterity. Patients begin to experience symptoms such as difficulties in writing. OBJECTIVE We developed a screening method for CTS using a tablet and stylus, focusing on writing motion, and verified its accuracy. METHODS We recruited 33 patients with CTS and 31 healthy volunteers for this study. The patients in the CTS group were diagnosed with CTS by hand surgeons in the orthopedic outpatient clinic based on physical examination and nerve conduction studies. 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 participants as non-CTS or CTS using a support vector machine. RESULTS Non-CTS and CTS were classified with 82% sensitivity and 71% specificity. The area under the curve was 0.81. CONCLUSIONS We proposed a CTS screening method that focuses on manual dexterity. This method can facilitate the screening for potential patients with CTS and provide a quantitative assessment of CTS.

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

10.2196/14172 ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. e14172 ◽  
Author(s):  
Koji Fujita ◽  
Takuro Watanabe ◽  
Tomoyuki Kuroiwa ◽  
Toru Sasaki ◽  
Akimoto Nimura ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Median Nerve ◽  
Carpal Tunnel ◽  
Cross Validation ◽  
Medical Examination ◽  
Support Vector ◽  
Screening System ◽  
Electrophysiological Testing ◽  
Tunnel Syndrome ◽  
Thenar Atrophy

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.

scholarly journals A Tablet-Based App for Carpal Tunnel Syndrome Screening: Diagnostic Case-Control Study (Preprint)

2019 ◽  
Author(s):  
Koji Fujita ◽  
Takuro Watanabe ◽  
Tomoyuki Kuroiwa ◽  
Toru Sasaki ◽  
Akimoto Nimura ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Median Nerve ◽  
Carpal Tunnel ◽  
Cross Validation ◽  
Medical Examination ◽  
Support Vector ◽  
Screening System ◽  
Electrophysiological Testing ◽  
Tunnel Syndrome ◽  
Thenar Atrophy

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.

scholarly journals Analgesic Drug Use for Carpal Tunnel Syndrome

2019 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Tamara Audrey Kadarusman ◽  
Hanik Badriyah Hidayati ◽  
Paulus Sugianto
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Median Nerve ◽  
Carpal Tunnel ◽  
Work Productivity ◽  
Analgesic Drugs ◽  
Drug Of Choice ◽  
Tunnel Syndrome ◽  
Analgesic Use

AbstractIntroduction: Carpal tunnel syndrome (CTS) is a group of neuropathic symptoms regarding to the compression of median nerve which passing through carpal tunnel. There has been a great number of prevalence of CTS in Indonesia, which leads to decreasing quality of life, lack of work productivity, and increasing health cost. Analgesic treatments have been drug of choice for carpal tunnel syndrome for years. However, the effectiveness of the drug and the risk of adverse effect of drugs have always been an issue for analgesic use. An observational study on profile of analgesic drugs administration for carpal tunnel syndrome patients in Dr. Soetomo General Hospital SurabayaMethod: A descriptive observational retrospective study has been conducted to observe the profile of analgesic drugs administration, including type and dosage of drugs, classification of drugs, drugs administration route, early and advanced type of analgesics, and duration of analgesic administration. Sociodemographic data and clinical characteristics (main symptoms) of carpal tunnel syndrome patients are also included in this study.Results: Out of 202 subjects of this study, most patients are women (84,16%), the group age of 50-59 to years old, and the most frequent job is household wife (43,56%). The most common analgesic drugs used for carpal tunnel syndrome patients is 50 mg sodium diclofenac for 78 patients (38.61%). All of those subjects are administered with oral analgesic (100%). 185 patients (91.59%) are administered with analgesic combinations. The duration of analgesic usage are 7 days as an early analgesic in 82 patients (40.59%).Conclusion: CTS is a syndrome due to median nerve compression of the hand, Women, household wife, and age of 50-59 years old are found to be vulnerable to this syndrome. Analgesic drugs mostly used is 50 mg natrium diclofenac, orally, combined, with the period of 7 days for early medication

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

10.2196/26320 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e26320
Author(s):  
Takafumi Koyama ◽  
Shusuke Sato ◽  
Madoka Toriumi ◽  
Takuro Watanabe ◽  
Akimoto Nimura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carpal Tunnel Syndrome ◽  
Data Collection ◽  
Anomaly Detection ◽  
Carpal Tunnel ◽  
Medical Condition ◽  
Screening Method ◽  
Detection Algorithm ◽  
Thenar Muscle ◽  
Tunnel Syndrome

Background Carpal tunnel syndrome (CTS) is a medical condition caused by compression of the median nerve in the carpal tunnel due to aging or overuse of the hand. The symptoms include numbness of the fingers and atrophy of the thenar muscle. Thenar atrophy recovers slowly postoperatively; therefore, early diagnosis and surgery are important. While physical examinations and nerve conduction studies are used to diagnose CTS, problems with the diagnostic ability and equipment, respectively, exist. Despite research on a CTS-screening app that uses a tablet and machine learning, problems with the usage rate of tablets and data collection for machine learning remain. Objective To make data collection for machine learning easier and more available, we developed a screening app for CTS using a smartphone and an anomaly detection algorithm, aiming to examine our system as a useful screening tool for CTS. Methods In total, 36 participants were recruited, comprising 36 hands with CTS and 27 hands without CTS. Participants controlled the character in our app using their thumbs. We recorded the position of the thumbs and time; generated screening models that classified CTS and non-CTS using anomaly detection and an autoencoder; and calculated the sensitivity, specificity, and area under the curve (AUC). Results Participants with and without CTS were classified with 94% sensitivity, 67% specificity, and an AUC of 0.86. When dividing the data by direction, the model with data in the same direction as the thumb opposition had the highest AUC of 0.99, 92% sensitivity, and 100% specificity. Conclusions Our app could reveal the difficulty of thumb opposition for patients with CTS and screen for CTS with high sensitivity and specificity. The app is highly accessible because of the use of smartphones and can be easily enhanced by anomaly detection.

scholarly journals Device Development for Detecting Thumb Opposition Impairment Using Carbon Nanotube-Based Strain Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3998
Author(s):  
Tomoyuki Kuroiwa ◽  
Akimoto Nimura ◽  
Yu Takahashi ◽  
Toru Sasaki ◽  
Takafumi Koyama ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Carpal Tunnel Syndrome ◽  
Correlation Coefficient ◽  
Carpal Tunnel ◽  
Medical Engineering ◽  
Strain Sensors ◽  
Device Development ◽  
Manual Inspection ◽  
Tunnel Syndrome ◽  
Thumb Motion

Research into hand-sensing is the focus of various fields, such as medical engineering and ergonomics. The thumb is essential in these studies, as there is great value in assessing its opposition function. However, evaluation methods in the medical field, such as physical examination and computed tomography, and existing sensing methods in the ergonomics field have various shortcomings. Therefore, we conducted a comparative study using a carbon nanotube-based strain sensor to assess whether opposition movement and opposition impairment can be detected in 20 hands of volunteers and 14 hands of patients with carpal tunnel syndrome while avoiding existing shortcomings. We assembled a measurement device with two sensors and attached it to the dorsal skin of the first carpometacarpal joint. We measured sensor expansion and calculated the correlation coefficient during thumb motion. The average correlation coefficient significantly increased in the patient group, and intrarater and interrater reliability were good. Thus, the device accurately detected thumb opposition impairment due to carpal tunnel syndrome, with superior sensitivity and specificity relative to conventional manual inspection, and may also detect opposition impairment due to various diseases. Additionally, in the future, it could be used as an easy, affordable, and accurate sensor in sensor gloves.

Correlation of clinical grading, physical tests and nerve conduction study in carpal tunnel syndrome

2018 ◽  
Vol 18 (3) ◽  
pp. 345-350 ◽  
Author(s):  
Sadegh Izadi ◽  
Bahareh Kardeh ◽  
Seied Saeed Hosini Hooshiar ◽  
Mojtaba Neydavoodi ◽  
Afshin Borhani-Haghighi
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Nerve Conduction Study ◽  
Nerve Conduction ◽  
Rating Scale ◽  
Screening Method ◽  
Cross Sectional ◽  
Physical Tests ◽  
Tunnel Syndrome ◽  
Clinical Grading

AbstractBackground and aimsCarpal tunnel syndrome (CTS) is a common debilitating condition. As the reliability of CTS-specific physical tests and its clinical grading remain a matter of debate, we determined the correlations between these assessments with nerve conduction study (NCS).MethodsIn this cross-sectional study, patients with uni or bilateral CTS, which was confirmed in electrodiagnosis, were enrolled. Clinical grading was based on the modified criteria of the Italian CTS Study Group. Numeric Pain Rating Scale (NPRS) and Boston Questionnaire (BQ) were used. Physical tests [Phalen’s, reverse Phalen’s, Tinel’s and manual carpal compression test (mCCT)] were performed by a single blinded neurologist. Ap-value<0.05 was considered statistically significant.ResultsA total of 100 patients (age=47.48±11.44 years; 85% female) with 181 involved hands were studied. The majority of hands (59.7%) were classified as grade 2 of clinical grading. On NCS, hands with mild (64%), moderate (27%) and severe (9%) CTS were identified. Sensory (velocity, latency and amplitude) and motor parameters (latency and amplitude) were significantly correlated with clinical grades (p-value<0.001). The correlation of NPRS (p-value=0.009) and BQ (p-value<0.001) scores with NCS was significant. None of the physical tests were significantly correlated with NCS in terms of result or duration (p-value>0.05).ConclusionsWe found that physical tests are not a reliable screening method for evaluation of CTS severity. However, the BQ and clinical grading can be more valuable due to their significant correlation with NCS.ImplicationsPhysicians might benefit from employing clinical grading and BQ in practice for better assessment of CTS severity.

scholarly journals Screening Method by Anomaly Detection for Patients with Carpal Tunnel Syndrome Using a Smartphone: Diagnostic Case-Control Study (Preprint)

2020 ◽  
Author(s):  
Takafumi Koyama ◽  
Shusuke Sato ◽  
Madoka Toriumi ◽  
Takuro Watanabe ◽  
Akimoto Nimura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carpal Tunnel Syndrome ◽  
Data Collection ◽  
Anomaly Detection ◽  
Carpal Tunnel ◽  
Medical Condition ◽  
Screening Method ◽  
Detection Algorithm ◽  
Thenar Muscle ◽  
Tunnel Syndrome

BACKGROUND Carpal tunnel syndrome (CTS) is a medical condition caused by compression of the median nerve in the carpal tunnel due to aging or overuse of the hand. The symptoms include numbness of the fingers and atrophy of the thenar muscle. Thenar atrophy recovers slowly postoperatively; therefore, early diagnosis and surgery are important. While physical examinations and nerve conduction studies are used to diagnose CTS, problems with the diagnostic ability and equipment, respectively, exist. Despite research on the application for screening CTS using a tablet and machine learning, problems with the usage rate of tablets and data collection for machine learning remain. OBJECTIVE To make data collection for machine learning easier and more available, we developed a screening application for CTS using a smartphone and an anomaly detection algorithm, and aimed to examine our system as a useful screening tool for CTS. METHODS In total, 36 CTS hands and 27 non-CTS hands were recruited. Participants controlled the character in our application using their thumbs. We recorded the position of the thumbs and time, and generated screening models that classify CTS and non-CTS using anomaly detection and an autoencoder and calculated the sensitivity, specificity, and area under the curve (AUC). RESULTS CTS and non-CTS participants were classified with 93% sensitivity, 69% specificity, and 0.86 AUC. When dividing the data by direction, the model with data in the same direction as the thumb opposition had the highest AUC of 0.99, 92% sensitivity, and 100% specificity. CONCLUSIONS Our application could reveal the difficulty of thumb opposition for CTS patients and screen for CTS with high sensitivity and specificity. The application is highly accessible because of the use of smartphones and can easily enhance machine learning using anomaly detection.

scholarly journals PHALEN TEST POSITIVATION TIME AND ITS CORRELATION WITH ELECTRONEUROMYOGRAPHY

2020 ◽  
Vol 28 (3) ◽  
pp. 114-116
Author(s):  
THÚLIO ADLEY LIMA CUNHA ◽  
OSVALDO MENDES DE OLIVEIRA FILHO ◽  
MARCELO BARBOSA RIBEIRO
Keyword(s):  
Surgical Treatment ◽  
Carpal Tunnel Syndrome ◽  
Observational Study ◽  
Carpal Tunnel ◽  
Outpatient Clinic ◽  
Treatment Level ◽  
Level Of Evidence ◽  
Cross Sectional ◽  
Tunnel Syndrome

ABSTRACT Objective: To compare the classification of CTS by the Phalen test with electromyography. Methods: Cross-sectional observational study. Patients at orthopedic outpatient clinic with carpal tunnel syndrome were evaluated by the Phalen test and compared with the result of the electroneuromyography. Results: Sample of 33 patients, mostly women (87.9%). Most patients were already diagnosed with severe CTS by ENMG. The results of the Phalen test and the electromyography were equal in 26 of the 33 patients (78.8%). Conclusion: The Phalen test showed its applicability, since it had results similar to those of ENMG in most cases, especially in the most severe ones. The exam studied is a possible tool for the classification and recommendation of surgical treatment. Level of evidence IV, Retrospective observational study.

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