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 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 Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

2021 ◽  
Vol 8 (11) ◽  
pp. 181
Author(s):  
Konstantinos I. Tsamis ◽  
Prokopis Kontogiannis ◽  
Ioannis Gourgiotis ◽  
Stefanos Ntabos ◽  
Ioannis Sarmas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Carpal Tunnel Syndrome ◽  
Median Nerve ◽  
Carpal Tunnel ◽  
Nerve Conduction ◽  
Automatic Detection ◽  
Nerve Conduction Studies ◽  
Machine Learning Techniques ◽  
Tunnel Syndrome

Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method.

scholarly journals When is needle examination of thenar muscle necessary in the evaluation of mild and moderate carpal tunnel syndrome?

2021 ◽  
Vol 67 (4) ◽  
pp. 518-525
Author(s):  
Zuhal Özişler ◽  
Müfit Akyüz
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Spontaneous Activity ◽  
Carpal Tunnel ◽  
Nerve Conduction ◽  
Sensory Nerve ◽  
Symptom Duration ◽  
Thenar Muscle ◽  
Distal Motor Latency ◽  
Motor Latency ◽  
Tunnel Syndrome

Objectives: This study aims to evaluate the predictors of standard nerve conduction study (NCS) parameters in determining the presence of axonal loss by means of spontaneous activity in patients with mild and moderate carpal tunnel syndrome (CTS). Patients and methods: Between May 2015 and April 2018, a total of 118 patients (11 males, 107 females; mean age: 52.3±10.6 years; range, 27 to 79 years) who underwent electrophysiological studies and were diagnosed with CTS were included. Demographic data of the patients including age, sex, and symptom duration were recorded. Electrodiagnostic studies were performed in all patients. All the needle electromyography (EMG) findings were recorded, but only the presence or absence of spontaneous EMG activities was used as the indicator of axonal injury. Results: In 37 (31.4%) of the patients, spontaneous activity was detected at the thenar muscle needle EMG. No spontaneous activity was observed in any of 43 (36.4%) patients with normal distal motor latency (DML). There were significant differences in DMLs, compound muscle action potential (CMAP) amplitudes, sensory nerve action potentials amplitudes, and sensory nerve conduction velocities between the groups with and without spontaneous activity (p<0.05). The multiple logistic regression analysis revealed that DML was a significant independent risk variable in determining presence of spontaneous activity. The most optimal cut-off value for median DML was calculated as 4.9 ms. If the median DML was >4.9 ms, the relative risk of finding spontaneous activity on thenar muscle needle EMG was 13.5 (95% CI: 3.6-51.2). Conclusion: Distal motor latency is the main parameter for predicting the presence of spontaneous activity in mild and moderate CTS patients with normal CMAP. Performing needle EMG of the thenar muscle in CTS patients with a DML of >4.9 ms may be beneficial to detect axonal degeneration in early stages.

scholarly journals Machine learning-based approach for disease severity classification of carpal tunnel syndrome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dougho Park ◽  
Byung Hee Kim ◽  
Sang-Eok Lee ◽  
Dong Young Kim ◽  
Mansu Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Rating Scale ◽  
External Validation ◽  
Gradient Boosting ◽  
Severity Classification ◽  
Extreme Gradient Boosting ◽  
Tunnel Syndrome ◽  
Multi Class Classification

AbstractIdentifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.

scholarly journals Prevalence and knowledge of carpal tunnel syndrome among the dental practitioners; survey based study in Riyadh

2019 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Dr. Anfal AlAnazi ◽  
Dr. Lujain AlZahrani ◽  
Dr. Heba Alshammary ◽  
Dr. Nadeen AlMaghamsi ◽  
Dr. Saja Alobaidi ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Work Experience ◽  
Medical Condition ◽  
Medical Practitioners ◽  
Work Related ◽  
Dental Practitioners ◽  
Common Medical Condition ◽  
Tunnel Syndrome ◽  
The City

Carpal Tunnel Syndrome (CTS) is a common medical condition that results in numbness, pain and tingling in the arm and constitutes about 90% of neuropathy entrapments. The condition arises when the median nerve in the arm is compressed. CTS can degenerate rapidly to cause nerve damage. The effects of CTS on medical practitioners can be serious given the speculation that the condition is work-related. Consequently, this study seeks to establish the prevalence and knowledge of CTS among dental practitioners in the city of Riyadh. In doing so, the research attempts to find relationships between CTS and workload and the experience of the dental practitioners. The study takes the model of sectional study that targets dental practitioners specifically in the city of Riyadh. The researchers recruited 190 dentists and used google forms to collect data from the respondents. The questions in the survey related to symptoms of the condition and the related demographics. The study found a significant comparison of the prevalence and knowledge of CTS among various subgroups. Daily patient exposure and work experience were some of the underlying features discovered in the study. Dentists with more work experience demonstrated significant knowledge on the condition and registered an equally high prevalence of pain associated with CST. The findings of the research show a direct correlation between workload, work experience, and CST.  

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.

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 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.

scholarly journals Infrared Thermography Based on Artificial Intelligence as a Screening Method for Carpal Tunnel Syndrome Diagnosis

2009 ◽  
Vol 37 (3) ◽  
pp. 779-790 ◽  
Author(s):  
B Jesenšek Papež ◽  
M Palfy ◽  
M Mertik ◽  
Z Turk
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Infrared Thermography ◽  
Carpal Tunnel ◽  
Screening Tool ◽  
Screening Method ◽  
Dorsal Side ◽  
Large Database ◽  
Computer Based ◽  
Tunnel Syndrome ◽  
Ergonomic Risk Factors

This study further evaluated a computer-based infrared thermography (IRT) system, which employs artificial neural networks for the diagnosis of carpal tunnel syndrome (CTS) using a large database of 502 thermal images of the dorsal and palmar side of 132 healthy and 119 pathological hands. It confirmed the hypothesis that the dorsal side of the hand is of greater importance than the palmar side when diagnosing CTS thermographically. Using this method it was possible correctly to classify 72.2% of all hands (healthy and pathological) based on dorsal images and > 80% of hands when only severely affected and healthy hands were considered. Compared with the gold standard electromyographic diagnosis of CTS, IRT cannot be recommended as an adequate diagnostic tool when exact severity level diagnosis is required, however we conclude that IRT could be used as a screening tool for severe cases in populations with high ergonomic risk factors of CTS.

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