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.

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 Usefulness of infrared thermography in diagnosing and evaluating severity of carpal tunnel syndrome

2021 ◽  
Vol 23 (2) ◽  
pp. 99-107
Author(s):  
Jiwon Yang ◽  
Yeong-Bae Lee ◽  
Young-Hee Sung ◽  
Dong-Jin Shin ◽  
Yong-Jin Kim ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Infrared Thermography ◽  
Carpal Tunnel ◽  
High Specificity ◽  
Complementary Design ◽  
Cutaneous Temperature ◽  
Background Pain ◽  
Tunnel Syndrome ◽  
Carpal Tunnel Questionnaire ◽  
Low Sensitivity

Background: Pain and autonomic dysfunction are prominent symptoms in some patients with carpal tunnel syndrome (CTS). Infrared thermography (IRT) has been used to evaluate CTS by measuring the cutaneous temperature and sympathetic vasomotor function.Methods: This study enrolled the 66 hands of 33 subjects, some of which had clinical CTS and the others were healthy. The enrolled patients completed the Boston Carpal Tunnel Questionnaire (BCTQ) and Historical-Objective scale, and underwent nerve conduction studies (NCSs) and IRT. Skin temperature was measured at the fingertips and the thenar and hypothenar regions in each hand. We analyzed (1) the correlations between self-reported severity, physician-assessed severity, and test results, and (2) the sensitivity and specificity of IRT in diagnosing CTS.Results: No significant correlation was observed between the results of the BCTQ, NCS, and IRT. IRT had a low sensitivity and high specificity in diagnosing CTS.Conclusions: IRT cannot replace NCS in diagnosing CTS, nor did it provide an advantage in combination with NCS. However, lower temperatures at the median nerve in some hands with moderate-to-severe CTS suggested the involvement of sympathetic nerve fiber function. Follow-up studies with a larger-scale and complementary design are required to elucidate the relationships.

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 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 Work-related risk factors for Carpal Tunnel Syndrome among Majmaah University female touchscreen users

2019 ◽  
Vol 35 (5) ◽  
Author(s):  
Walaa Sayed Mohammad
Keyword(s):  
Risk Factors ◽  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Wrist Flexion ◽  
Creative Commons ◽  
Work Related ◽  
Related Risk ◽  
Computer Based ◽  
Tunnel Syndrome ◽  
Phalen's Test

Objectives: This study aimed to explore the prevalence of carpal tunnel syndrome symptoms among female touchscreen users at Majmaah University, Saudi Arabia and to make a comparison for the wrist range of motion between probable CTS and non-CTS female touchscreen users. Methods: Two hundred and twenty-two female touchscreen users were enrolled in the present study. Among this cohort, fifty-two were academic members, 40 were employees and 130 were undergraduates. A Digital Inclinometer device was used to assess ROM of the wrist movements. A computer-based questionnaire, Phalen’s test, and Tinel’s sign were used to investigate the presence of CTS symptoms. The study was conducted between November 2018 and February 2019 at Majmaah University. Results: The prevalence of probable CTS was 34.2% among touchscreen users; the percent of probable CTS was significantly higher in undergraduates compared to other touchscreen users. There was a significant reduction in wrist flexion between the tested groups. Conclusion: Female touchscreen users at Majmaah University tended to have a high-risk for CTS. Wrist ROM measurements, particularly wrist flexion, could be a beneficial indicator for anticipating deviations in wrist posture after long-term touchscreen use. It is necessary to consider the job nature, age, BMI, and duration of using touchscreen as risk factors for CTS symptoms. doi: https://doi.org/10.12669/pjms.35.5.683 How to cite this:Mohammad WS. Work-related risk factors for Carpal Tunnel Syndrome among Majmaah University female touchscreen users. Pak J Med Sci. 2019;35(5):---------. doi: https://doi.org/10.12669/pjms.35.5.683 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

Sign in / Sign up

Export Citation Format

Share Document