Potential Use of a New Energy Vision (NEV) Camera for Diagnostic Support of Carpal Tunnel Syndrome: Development of a Decision‐Making Algorithm to Differentiate Carpal Tunnel‐Affected Hands from Controls

Background: /Objectives: Carpal Tunnel Syndrome (CTS) is a prevalent neuropathy often requiring invasive diagnostics. This study evaluates the New Energy Vision (NEV) camera, a non-invasive imaging tool, for detecting CTS by analyzing visible light images of the hand, aiming to develop a decision-making algorithm to differentiate CTS-affected hands from controls. Methods: A two-part study was conducted with 103 participants (50 controls, 53 CTS patients) in Part 1, using NEV camera images to train a Support Vector Machine (SVM) classifier. Part 2 compared median nerve-damaged (MED) and ulnar nerve-normal (ULN) palm areas in 32 CTS patients. Clinical validations included nerve conduction tests (NCT), Semmes-Weinstein monofilament testing (SWMT), and Boston Carpal Tunnel Questionnaire (BCTQ). Results: The SVM classifier achieved 93.33% accuracy (confusion matrix: [[14, 1], [1, 14]]). Cross-validation showed a mean accuracy of 81.79%. Part 2 revealed significant differences (p < 0.05) in color proportions and Haralick texture features between MED and ULN areas. BCTQ scores and SWMT grades further supported the differentiation. Conclusions: The NEV camera, combined with machine learning, offers a promising non-invasive method for CTS diagnosis, capturing distinct image features linked to nerve damage.