An AI-Driven Pipeline for Localization, Segmentation, and Classification of Carpal Tunnel Syndrome Using Ultrasound Images of the Median Nerve.

Abstract

Carpal tunnel syndrome (CTS), the most common peripheral neuropathy, is currently diagnosed by clinical suspicion supported by tools such as the CTS-6 questionnaire and ultrasound. While these tools are widely used and clinically valuable, diagnostic accuracy may vary based on examiner experience and technique. This study introduces a pilot artificial intelligence (AI)-driven ultrasound pipeline using YOLOv11 for localization, U-Net for segmentation, and ConvNeXt for classification to support CTS diagnosis. In this observational cohort study, patients ≥18 years were screened for CTS from June 2023 to September 2024 by a board-certified hand surgeon using the CTS-6, and ultrasound images of the median nerve were obtained at the carpal tunnel inlet. One hundred twenty-one wrists (73 with CTS/48 without CTS) were used for model training and validation. The AI pipeline sequentially localized the median nerve (YOLOv11), segmented nerve boundaries (U-Net), and classified CTS status (ConvNeXt). The ConvNeXt classification model achieved an accuracy of 94.1%, positive predictive value (PPV) of 0.86, and sensitivity of 1.0. The YOLOv11 localization model demonstrated PPV of 0.95 and sensitivity of 0.98. The U-Net segmentation model achieved a validation Intersection over Union (IoU) of 0.86. This pilot study demonstrates that an AI-assisted ultrasound pipeline can achieve strong diagnostic performance for CTS. Future work with larger, multi-center datasets and enhanced model interpretability is warranted prior to clinical deployment.