Carpal Tunnel Release Despite Normal Nerve Conduction Studies in Carpal Tunnel Syndrome Patients

Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome

Carpal tunnel syndrome (CTS) is a common peripheral nerve disease in adults; it can cause pain, numbness, and even muscle atrophy and will adversely affect patients' daily life and work. There are no standard diagnostic criteria that go against the early diagnosis and treatment of patients. MRI as a novel imaging technique can show the patient's condition more objectively, and several characteristics of carpal tunnel syndrome have been found. However, various image sequences, heavy artifacts, small lesion characteristics, high volume of imagine reading, and high difficulty in MRI interpretation limit its application in clinical practice. With the development of automatic image segmentation technology, the algorithm has great potential in medical imaging. The challenge is that the segmentation target is too small, and there are two categories of images with the proximal border of the carpal tunnel as the boundary. To meet the challenge, we propose an end-to-end deep learning framework called Deep CTS to segment the carpal tunnel from the MR image. The Deep CTS consists of the shape classifier with a simple convolutional neural network and the carpal tunnel region segmentation with simplified U-Net. With the specialized structure for the carpal tunnel, Deep CTS can segment the carpal tunnel region efficiently and improve the intersection over union of results. The experimental results demonstrated that the performance of the proposed deep learning framework is better than other segmentation networks for small objects. We trained the model with 333 images, tested it with 82 images, and achieved 0.63 accuracy of intersection over union and 0.17 s segmentation efficiency, which indicate great promise for the clinical application of this algorithm.

Comparison of characteristics between neuropathic pain and non-neuropathic pain in patients with diabetic carpal tunnel syndrome: A cross-sectional study

Background

The aim of the study was to compare the clinical characteristics of diabetic carpal tunnel syndrome between patients with neuropathic pain (NeuP) and non-NeuP.

Methods

We enrolled 276 patients with diabetic carpal tunnel syndrome. Pain symptoms were evaluated using a visual analog scale. Douleur Neuropathique 4, the Neuropathic Pain Symptoms Inventory questionnaire, and the body map were used to assess neuropathic symptoms. Baseline information, clinical manifestations, electrophysiological test results, and psychological status were compared between the neuropathic pain (NeuP) and non-NeuP to identify the risk factor for NeuP occurrence.

Results

Results showed that the degree of pain was more severe in NeuP patients than in nociceptive pain patients (p = 0.025). The frequencies of light touch and pinprick were more pronounced in the NeuP group than in the non-NeuP group (light touch: p = 0.001; pinprick: p = 0.004). There were 48 and 27 NeuP patients with extramedian and proximal spread, respectively, whereas in the non-NeuP group, there were 11 and 9 patients, respectively (p = 0.03). Electrophysiological results showed that patients in the NeuP group exhibited greater sensory nerve conduction velocity impairment compared with the non-NeuP group (p = 0.033). Pain Catastrophizing Scale total scores of the NeuP group were significantly higher than those of the non-NeuP group (p = 0.006).

Conclusion

Of the 276 diabetic carpal tunnel syndrome patients studied, the majority had NeuP. Furthermore, light touch, electrophysiological test results, and psychological factors were found to be related to NeuP occurrence in patients with diabetic carpal tunnel syndrome.