Hand (New York, N.Y.) · 2026
An AI-Driven Pipeline for Localization, Segmentation, and Classification of Carpal Tunnel Syndrome Using Ultrasound Images of the Median Nerve.
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Hyzny Rachel, Patterson Charles J, Kishor Abhigyan, Littlefield Nickolas, Nwosu Chinemerem, Weinberg Jacob, Tafti Ahmad P, Fowler John R
Аннотация
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.