B-Mode ultrasound imaging in diagnosing carpal tunnel syndrome: an auxiliary diagnostic tool for hand surgeons

Objective

The purpose of this article is to explore the effectiveness of B-Mode ultrasound as an auxiliary diagnostic tool for carpal tunnel syndrome (CTS). It aims to demonstrate the advantages of B-Mode ultrasound, including its non-invasive nature and its ability to provide real-time imaging, in localizing nerve compression and predicting postoperative outcomes.

Methods

The study included 40 patients who were subjected to preoperative B-ultrasonography. The approach focused on evaluating the consistency of B-Mode ultrasound results with intraoperative findings. It also assessed the importance of employing standardized imaging techniques and emphasized the need for cooperation between hand surgeons and sonographers for accurate diagnosis.

Results

B-Mode ultrasound findings in the study were consistent with intraoperative observations, indicating its reliability. Additionally, B-Mode ultrasound was able to identify other anatomical abnormalities within the carpal canal that may contribute to CTS symptoms, such as persistent median arteries, median nerve bifurcation, and space-occupying lesions like cysts and tumors.

Conclusion

The article concludes that B-Mode ultrasound should be considered a valuable supplementary diagnostic tool for CTS, particularly in instances where clinical signs and electrophysiological studies do not offer clear results. However, it should not replace established diagnostic methods for CTS.

Electrophysiological assessment of radial shock wave therapy for carpal tunnel syndrome

This study presents an electrophysiological assessment of radial extracorporeal shock wave therapy on patients with carpal tunnel syndrome (CTS). Sixteen CTS subjects received radial extracorporeal shock wave therapy once a week for five consecutive weeks. Outcome performance was assessed using the Boston Carpal Tunnel Questionnaire (BCTQ) and electrodiagnostic measurements including a nerve conduction study of the median nerve and a compound muscle action potential (CMAP) scan of the abductor pollicis brevis muscle. The BCTQ and the sensory conduction test measurements were all statistically improved after the treatment. However, the motor conduction test measurements were not significantly different before and after the treatment. The CMAP scan examination revealed MScanFit motor unit number estimation (MUNE) was significantly higher after the treatment, while no significant change was found in StairFit MUNE and step index. These results confirmed the effectiveness of shock wave therapy for treating CTS symptoms and the associated sensory property changes. The reasons for the inconsistencies from different CMAP scan processing methods are worthwhile targets for further investigation.

Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome

Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients' health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.

Carpal Tunnel Syndrome Associated with Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) have transformed the therapeutic approach to diverse malignancies, leading to substantial enhancements in patient prognosis. However, along with their benefits, ICIs also increase the incidence of immune-related adverse events (irAEs). In the present paper, we highlight four cases of carpal tunnel syndrome (CTS) as an uncommon manifestation of toxicity induced by ICIs. Although diagnosed with different malignancies, the patients were undergoing ICI therapy when they developed CTS-consistent side effects accompanied by severe neuropathy. Prompt treatment with corticosteroids, intravenous immunoglobulins, or methotrexate resulted in complete symptomatic relief for all patients. This article therefore emphasizes the importance of recognizing and managing rare adverse events associated with ICI use to ensure optimal patient care.

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman's correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.

Research advances and trends in the surgical treatment of carpal tunnel syndrome from 2003 to 2022: A CiteSpace-based bibliometric analysis

Background

Carpal Tunnel Syndrome (CTS) is one of the most common peripheral neuropathies. The typical symptoms are tingling and numbness in the median nerve distribution of the hand. Current treatment for CTS includes general conservative treatment and surgical treatment. Surgical treatment plays a crucial role in the management of CTS, but little bibliometric analysis has been conducted on it. Therefore, this study aimed to map the literature co-citation network using CiteSpace (6.1 R4) software. Research frontiers and trends were identified by retrieving subject headings with significant changing word frequency trends, which can be used to predict future research advances in the surgical treatment of CTS.

Methods

Publications on the surgical treatment of CTS in the Web of Science database were collected between 2003 and 2022. CiteSpace software was applied to visualize and analyze publications, countries, institutions, journals, authors, references, and keywords.

Results

A total of 336 articles were collected, with the USA being the major publishing power in all countries/regions. JOURNAL OF HAND SURGERY AMERICAN VOLUME was the journal with the most published and co-cited articles. Based on keyword and reference co-citation analysis, keywords such as CTS, surgery, release, median nerve, and diagnosis were the focus of the study.

Conclusion

The results of this bibliometric study provide clinical research advances and trends in the surgical treatment of patients with CTS over the past 20 years, which may help researchers to identify hot topics and explore new directions for future research in the field.

Augmented Diagnostic Accuracy of Ultrasonography for Diagnosing Carpal Tunnel Syndrome Using an Optimised Wrist Circumference-Dependent Cross-Sectional Area Equation

Introduction: In diagnosing carpal tunnel syndrome (CTS) there is no consensus about the upper limit of normal (ULN) of the cross-sectional area (CSA) of the median nerve at the carpal tunnel inlet. A previous study showed wrist circumference is the most important independent predictor for the ULN. In this study we optimised a wrist circumference-dependent ULN equation for optimal diagnostic accuracy and compared it to the generally used fixed ULN of 11 mm².

Methods: CSA and wrist circumference were measured in a prospective cohort of 253 patients (clinically defined CTS) and 96 healthy controls. An equation for the ULN for CSA was developed by means of univariable regression analysis. We calculated z-scores for all patients and healthy controls, and analysed these scores in a ROC curve and a decision plot. Sensitivity and specificity were determined and compared to fixed ULN values.

Results: We found augmented diagnostic accuracy of our newly developed equation y = 0.88 ^* x −4.0, where y = the ULN of the CSA and x = wrist circumference. This equation has a corresponding sensitivity and specificity of 75% compared to a sensitivity of 70% while using a fixed cut-off value of 11 mm² (p = 0.015).

Conclusion: Optimising the regression equation for wrist circumference-dependent ULN cross-sectional area of the median nerve at the wrist inlet might improve diagnostic accuracy of ultrasonography in patients with carpal tunnel syndrome and seems to be more accurate than using fixed cut-off values.

Diagnosis and Management of Carpal Tunnel Syndrome in Children with Mucopolysaccharidosis: A 10 Year Experience

INTRODUCTION

Mucopolysaccharidoses (MPS) are rare and clinically heterogeneous lysosomal storage disorders. Carpal tunnel syndrome (CTS) is a frequent complication in MPS types I, II, VI, and VII. CTS symptoms are difficult to recognize in these children, and often there is a lack of appropriate investigations.

PATIENTS AND METHODS

In this retrospective study, all MPS patients were referred to the electrodiagnostic (EDX) laboratory of a single academic center during a 10-year period. Forty-eight children underwent serial EDX studies for CTS diagnosis and follow-up after surgery. Forty-two patients were diagnosed with CTS. Sensory nerve conduction velocity (SNCV), distal motor latency (DML), and motor nerve conduction velocity through the wrist (MNCV-W) of the median nerve were reviewed and analyzed.

RESULTS

One-hundred-three EDX examinations were performed on 48 patients. The median age at disease diagnosis was 2.1 years versus 4.9 years for CTS diagnosis. Analysis of the series revealed that electrophysiological abnormalities of CTS could have started much earlier (before the age of 2 years or at diagnosis of MPS). Diagnosis was based on SNCV and DML results, and MNCV-W was taken into consideration. Bilateral CTS was frequent (88%) in the types of MPS studied in our population and was observed from the first year of life, and may not have be associated with obvious clinical symptoms. EDX studies also helped in the follow-up and detection of CTS relapses, thus leading to an early intervention allowing a better recovery.

CONCLUSION

EDX studies should be performed promptly and regularly in these patients. Prospective studies are required in order to understand the effect of disease-specific therapies in preventing the development of CTS in these patients.

SYNOPSIS

EDX studies should be performed in MPS patients soon after diagnosis and during routine follow-up, before and after surgical decompression.

Persistent median artery thrombosis: A rare cause of carpal tunnel syndrome

Background: Carpal tunnel syndrome (CTS) is a sporadic event with compression of the median nerve (MN). Persistent median artery (PMA) thrombosis is an exceptionally rare cause of CTS. Case report: 38-year-old male presented with acute on subacute right wrist pain with positive Tinel's sign. An ultrasound and computed angiography study confirmed a PMA with thrombosis. The patient was treated with intravenous heparin then discharged home on enoxaparin and warfarin crossover. Discussion: PMA can lead to CTS by compression from the adjacent median nerve. Thrombosis of the PMA can also lead to CTS. Surgical intervention is needed in cases of severe CTS. Carpal tunnel release is usually successful. Excision of the PMA can risk vascular compromise of the digits. Ultrasound is excellent for detecting rare causes of CTS. Conclusion: Ultrasound examination for CTS should include search for PMA and associated anatomical variations.

Neurologic complications of acute and chronic renal disease

There is an increasing incidence and prevalence of patients with chronic kidney disease (CKD) in Western industrialized countries and currently is estimated at approximately 10% of adults aged over 20 years. Renal failure causes an excessively increased risk of cerebrovascular and cardiovascular complications. Moreover, renal failure leads to a number of the neurologic symptoms neurologists are often confronted with. This chapter gives an overview of possible neurologic complications of acute renal failure and CKD. Complications of the central nervous system (e.g., uremic encephalopathy, disequilibrium syndrome, and drug induced disorders) are reviewed. It has long been known that uremia leads to peripheral nerve injury. Frequent neurological diseases such as uremic polyneuropathy, autonomic neuropathy, and a range of mononeuropathies are discussed.