Towards Diagnosis of Carpal Tunnel Syndrome Using Machine Learning

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.

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

Scientific Reports ◽

10.1038/s41598-021-97043-7 ◽

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.

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

JMIR mhealth and uhealth ◽

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.

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

10.2196/preprints.26320 ◽

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.

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Clinical Update: Assessing Maximum Medical Improvement in Carpal Tunnel Syndrome

Guides Newsletter ◽

10.1001/amaguidesnewsletters.2003.julaug02 ◽

2003 ◽

Vol 8 (4) ◽

pp. 4-5

Author(s):

Christopher R. Brigham ◽

James B. Talmage

Keyword(s):

Carpal Tunnel Syndrome ◽

Carpal Tunnel ◽

Proper Time ◽

Carpal Tunnel Release ◽

Hand Function ◽

Factors Affecting ◽

Early Results ◽

Medical Evaluation ◽

Nerve Recovery ◽

Tunnel Syndrome

Abstract Permanent impairment cannot be assessed until the patient is at maximum medical improvement (MMI), but the proper time to test following carpal tunnel release often is not clear. The AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) states: “Factors affecting nerve recovery in compression lesions include nerve fiber pathology, level of injury, duration of injury, and status of end organs,” but age is not prognostic. The AMA Guides clarifies: “High axonotmesis lesions may take 1 to 2 years for maximum recovery, whereas even lesions at the wrist may take 6 to 9 months for maximal recovery of nerve function.” The authors review 3 studies that followed patients’ long-term recovery of hand function after open carpal tunnel release surgery and found that estimates of MMI ranged from 25 weeks to 24 months (for “significant improvement”) to 18 to 24 months. The authors suggest that if the early results of surgery suggest a patient's improvement in the activities of daily living (ADL) and an examination shows few or no symptoms, the result can be assessed early. If major symptoms and ADL problems persist, the examiner should wait at least 6 to 12 months, until symptoms appear to stop improving. A patient with carpal tunnel syndrome who declines a release can be rated for impairment, and, as appropriate, the physician may wish to make a written note of this in the medical evaluation report.

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Clinical Update: Understanding Causation: An Example Using Carpal Tunnel Syndrome

Guides Newsletter ◽

10.1001/amaguidesnewsletters.2007.novdec02 ◽

2007 ◽

Vol 12 (6) ◽

pp. 5-8 ◽

Cited By ~ 3

Author(s):

J. Mark Melhorn

Keyword(s):

Risk Factors ◽

Carpal Tunnel Syndrome ◽

Carpal Tunnel ◽

Legal Responsibility ◽

Medical Evidence ◽

Anatomic Structure ◽

Legal Professionals ◽

Compensation Systems ◽

Tunnel Syndrome ◽

Four Levels

Abstract Medical evidence is drawn from observation, is multifactorial, and relies on the laws of probability rather than a single cause, but, in law, finding causation between a wrongful act and harm is essential to the attribution of legal responsibility. These different perspectives often result in dissatisfaction for litigants, uncertainty for judges, and friction between health care and legal professionals. Carpal tunnel syndrome (CTS) provides an example: Popular notions suggest that CTS results from occupational arm or hand use, but medical factors range from congenital or acquired anatomic structure, age, sex, and body mass index, and perhaps also involving hormonal disorders, diabetes, pregnancy, and others. The law separately considers two separate components of causation: cause in fact (a cause-and-effect relationship exists) and proximate or legal cause (two events are so closely related that liability can be attached to the first event). Workers’ compensation systems are a genuine, no-fault form of insurance, and evaluators should be aware of the relevant thresholds and legal definitions for the jurisdiction in which they provide an opinion. The AMA Guides to the Evaluation of Permanent Impairment contains a large number of specific references and outlines the methodology to evaluate CTS, including both occupational and nonoccupational risk factors and assigning one of four levels of evidence that supports the conclusion.

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