Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel.

Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level / Smerilli, Gianluca; Cipolletta, Edoardo; Sartini, Gianmarco; Moscioni, Erica; Di Cosmo, Mariachiara; Fiorentino, Maria Chiara; Moccia, Sara; Frontoni, Emanuele; Grassi, Walter; Filippucci, Emilio. - In: ARTHRITIS RESEARCH & THERAPY. - ISSN 1478-6362. - 24:1(2022), p. 38. [10.1186/s13075-022-02729-6]

Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level

Smerilli, Gianluca;Cipolletta, Edoardo;Sartini, Gianmarco;Moscioni, Erica;Di Cosmo, Mariachiara;Fiorentino, Maria Chiara;Moccia, Sara;Frontoni, Emanuele;Grassi, Walter;Filippucci, Emilio
2022-01-01

Abstract

Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/304383
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