Classifying median nerves in carpal tunnel syndrome: Ultrasound image analysis

Rationale and objectives: Carpal tunnel syndrome (CTS) refers to a common median nerve pathology, which is related to an increased pressure in the fibrous/bone canal of the wrist. Ultrasound gained popularity recently as a useful tool for the accurate and repetitive diagnosis of carpal tunnel syndrome. The present study aimed to develop an objective, repetitive technique for assessing median nerves based on carpal tunnel ultrasound texture analysis. Material and methods: Sixty ultrasound images, including 30 images of swollen ‘‘symptomatic” median nerves and 30 normal ‘‘asymptomatic” median nerves, were used in this study. Narrow age group of patients were selected. They were recruited after positive nerve conduction study and with present clinical symptoms reviled on basis of interview and written questionnaire. Meticulous nerve area and echogenicity assessment were conducted in line with existing recommendations. Results: Using the feature-selection tool MaZda, an exhaustive search of the data space was conducted, and four texture features were found for which the classification was the most accurate. Images were classified using a support vector machine with a five-fold cross-verification in MATLAB. Evoked outcomes showed a 79% correct classification rate. Conclusion: Computer analysis of the image echogenicity of the median nerve presented confidence levels comparable to trusted evaluation techniques. Further, it is a promising tool for assessing the nerve’s status in CTS as approach of the CTS assessment free from subjectivity of examiner. The developed method enables nerve classification based on echogenicity that reflects the nerve composition changes not only subjective nerve area assessment.

---

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).