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.

Palmar Musculature: Does It Affect the Development of Carpal Tunnel Syndrome? A Pilot Study

2021 ◽  
Author(s):  
Trevor Simcox ◽  
Lauren Seo ◽  
Kevin Dunham ◽  
Shengnan Huang ◽  
Catherine Petchprapa ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Correlation Coefficient ◽  
Carpal Tunnel ◽  
Case Control Study ◽  
Anatomical Variation ◽  
Case Control ◽  
Transverse Carpal Ligament ◽  
Large Variability ◽  
Tunnel Syndrome ◽  
Control Study

Abstract Background The etiology of carpal tunnel syndrome (CTS) is multifactorial. Static mechanical characteristics of CTS have been described, but dynamic (muscular) parameters remain obscure. We believe that musculature overlying the transverse carpal ligament may have an effect on carpal tunnel pressure and may explain the prevalence of CTS in manual workers. Questions/Purposes To utilize magnetic resonance imaging (MRI) imaging to estimate the amount of muscle crossing the area of the carpal tunnel and to compare these MRI measurements in patients with and without documented CTS. Methods A case–control study of wrist MRI scans between January 1, 2018, and December 1, 2019, was performed. Patients with a diagnosis of CTS were matched by age and gender with controls without a diagnosis of CTS. Axial MRI cuts at the level of the hook of the hamate were used to measure the thenar and hypothenar muscle depth overlying the carpal tunnel. Muscle depth was quantified in millimeters at three points: midcapitate, capitate–hamate border, capitate–trapezoid border. Average depth was calculated by dividing the cross-sectional area (CSA) by the transverse carpal ligament width. Statistical analysis included Student's t-test, chi-square test, and Pearson's correlation coefficient calculation. Results A total of 21 cases and 21 controls met the inclusion criteria for the study. There were no significant differences in demographics between case and control groups. The location and depth of the musculature crossing the carpal tunnel were highly variable in all areas evaluated. A significantly positive correlation was found between proximal median nerve CSA and muscle depth in the capitate–hamate area (correlation coefficient = 0.375; p = 0.014). CSA was not significantly associated with chart documented CTS. Conclusions We found large variability in our measurements. This likely reflects true anatomical variation. The significance of our findings depends on the location of the muscles and the line of pull and their effect on the mechanics of the transverse carpal ligament. Future research will focus on refining measurement methodology and understanding the mechanical effect of the muscular structure and insertions on carpal tunnel pressure. Level of Evidence This is a Level 3, case–control study.

KATZ AND STIRRAT HAND DIAGRAM REVISITED

Hand Surgery ◽  
2010 ◽  
Vol 15 (02) ◽  
pp. 71-73 ◽  
Author(s):  
Rouin Amirfeyz ◽  
Sanchit Mehendale ◽  
Sarah Tyrrell ◽  
Raj Bhatia ◽  
Ian Leslie ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Correlation Coefficient ◽  
Carpal Tunnel ◽  
Observer Agreement ◽  
Healthy Individuals ◽  
Intra Class Correlation ◽  
Hand Surgeon ◽  
Observer Reliability ◽  
Tunnel Syndrome ◽  
Intra Class Correlation Coefficient

Katz and Stirrat devised a hand diagram, which uses subjective information from the patient for the diagnosis of carpal tunnel syndrome (CTS). They reported a good result. We tested the inter-observer and intra-observer reliability of this hand diagram. Twenty five consecutive patients with a diagnosis of CTS, 25 with other common hand and wrist problems but CTS and 25 healthy individuals were prospectively recruited. Each patient filled in a hand diagram. Two experienced hand surgeon scored the diagrams blindly on two different sittings four-week apart. The intra-class correlation coefficient (ICC) and Cohen's Kappa were used for intra and inter-observer reliability respectively. The intra-observer agreement was poor (ICC 95% 0.33–0.65) and inter-observer was fair (Kappa = 0.241). This study does not support the use of Katz and Stirrat Hand Diagram for the diagnosis of carpal tunnel syndrome in place if a thorough clinical examination.

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.

Three-dimensional analysis of thumb motion recovery after carpal tunnel release

2021 ◽  
pp. 175319342110147
Author(s):  
Akira Kodama ◽  
Hiroshi Kurumadani ◽  
Teruyasu Tanaka ◽  
Rikuo Shinomiya ◽  
Toru Sunagawa ◽  
...  
Keyword(s):  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Path Length ◽  
Carpal Tunnel Release ◽  
Three Dimensional ◽  
Motor Dysfunction ◽  
Joint Movement ◽  
Flexion Extension ◽  
Tunnel Syndrome ◽  
Thumb Motion

This study quantified recovery of thumb motion in patients with carpal tunnel syndrome after carpal tunnel release using three-dimensional motion analysis with a retroreflective surface-based marker method. Eighteen hands from 14 patients who underwent carpal tunnel release for idiopathic carpal tunnel syndrome were included. The angular movements of the three joints of the thumb, the path length of the thumb tip and the area enclosed by the perimeter path of the thumb tip were measured during circumduction. The range of joint movement, including abduction/adduction of the trapeziometacarpal joint, and flexion/extension of the interphalangeal and metacarpophalangeal joints and the path length of the thumb tips, improved significantly 1 year after surgery. The quantification of thumb kinematics helps to better understand motor dysfunction in carpal tunnel syndrome, assess the severity of the condition and decide on treatment. Level of evidence: IV

Clinical Update: Assessing Maximum Medical Improvement in Carpal Tunnel Syndrome

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.

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

2007 ◽  
Vol 12 (6) ◽  
pp. 5-8 ◽  
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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