{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T14:16:56Z","timestamp":1789827416762,"version":"4.0.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Arthritis Res Ther"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Consecutive patients with rheumatic and musculoskeletal disorders were recruited. Transverse US images were acquired at the carpal tunnel inlet, and the CSA was manually measured. Anatomical variants were registered. The dataset consisted of 246 images (157 for training, 40 for validation, and 49 for testing) from 103 patients each associated with manual annotations of the nerve boundary. A Mask R-CNN, state-of-the-art CNN for image semantic segmentation, was trained on this dataset to accurately localize and segment the median nerve section. To evaluate the performances on the testing set, precision (\n                      <jats:italic>Prec<\/jats:italic>\n                      ), recall (\n                      <jats:italic>Rec<\/jats:italic>\n                      ), mean average precision (\n                      <jats:italic>mAP<\/jats:italic>\n                      ), and Dice similarity coefficient (\n                      <jats:italic>DSC<\/jats:italic>\n                      ) were computed. A sub-analysis excluding anatomical variants was performed. The CSA was automatically measured by the algorithm.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The algorithm correctly identified the median nerve in 41\/49 images (83.7%) and in 41\/43 images (95.3%) excluding anatomical variants. The following metrics were obtained (with and without anatomical variants, respectively): Prec 0.86 \u00b1 0.33 and 0.96 \u00b1 0.18, Rec 0.88 \u00b1 0.33 and 0.98 \u00b1 0.15, mAP 0.88 \u00b1 0.33 and 0.98 \u00b1 0.15, and DSC 0.86 \u00b1 0.19 and 0.88 \u00b1 0.19. The agreement between the algorithm and the sonographer CSA measurements was excellent [ICC 0.97 (0.94\u20130.98)].<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The developed algorithm has shown excellent performances, especially if excluding anatomical variants. Future research should aim at expanding the US image dataset including a wider spectrum of normal anatomy and pathology. This deep learning approach has shown very high potentiality for a fully automatic support for US assessment of carpal tunnel syndrome.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13075-022-02729-6","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T05:03:44Z","timestamp":1644296624000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level"],"prefix":"10.1186","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3972-4458","authenticated-orcid":false,"given":"Gianluca","family":"Smerilli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6881-8197","authenticated-orcid":false,"given":"Edoardo","family":"Cipolletta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gianmarco","family":"Sartini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica","family":"Moscioni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0269-4304","authenticated-orcid":false,"given":"Mariachiara","family":"Di Cosmo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7397-803X","authenticated-orcid":false,"given":"Maria Chiara","family":"Fiorentino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4494-8907","authenticated-orcid":false,"given":"Sara","family":"Moccia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-9244","authenticated-orcid":false,"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter","family":"Grassi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7251-7784","authenticated-orcid":false,"given":"Emilio","family":"Filippucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"key":"2729_CR1","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.mcna.2018.10.012","volume":"103","author":"CT Doughty","year":"2019","unstructured":"Doughty CT, Bowley MP. Entrapment neuropathies of the upper extremity. Med Clin North Am. 2019;103:357\u201370. https:\/\/doi.org\/10.1016\/j.mcna.2018.10.012.","journal-title":"Med Clin North Am"},{"key":"2729_CR2","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1016\/S1474-4422(16)30231-9","volume":"15","author":"L Padua","year":"2016","unstructured":"Padua L, Coraci D, Erra C, Pazzaglia C, Paolasso I, Loreti C, et al. Carpal tunnel syndrome: clinical features, diagnosis, and management. Lancet Neurol. 2016;15:1273\u201384. https:\/\/doi.org\/10.1016\/S1474-4422(16)30231-9.","journal-title":"Lancet Neurol"},{"key":"2729_CR3","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s11999-010-1637-5","volume":"469","author":"JR Fowler","year":"2011","unstructured":"Fowler JR, Gaughan JP, Ilyas AM. The sensitivity and specificity of ultrasound for the diagnosis of carpal tunnel syndrome: a meta-analysis. Clin Orthop Relat Res. 2011;469:1089\u201394. https:\/\/doi.org\/10.1007\/s11999-010-1637-5.","journal-title":"Clin Orthop Relat Res"},{"issue":"7","key":"2729_CR4","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1016\/j.ultrasmedbio.2012.02.026","volume":"38","author":"TW Tai","year":"2012","unstructured":"Tai TW, Wu CY, Su FC, Chern TC, Jou IM. Ultrasonography for diagnosing carpal tunnel syndrome: a Meta-analysis of diagnostic test accuracy. Ultrasound Med Biol. 2012;38(7):1121\u20138. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2012.02.026.","journal-title":"Ultrasound Med Biol"},{"key":"2729_CR5","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.semarthrit.2011.11.006","volume":"41","author":"A Descatha","year":"2012","unstructured":"Descatha A, Huard L, Aubert F, Barbato B, Gorand O, Chastang JF. Meta-analysis on the performance of sonography for the diagnosis of carpal tunnel syndrome. Semin Arthritis Rheum. 2012;41:914\u201322. https:\/\/doi.org\/10.1016\/j.semarthrit.2011.11.006.","journal-title":"Semin Arthritis Rheum"},{"issue":"2","key":"2729_CR6","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1002\/mus.23389","volume":"46","author":"C French","year":"2012","unstructured":"French C, Cartwright MS, Hobson-Webb LD, Boon AJ, Alter KE, Hunt CH, et al. Evidence-based guideline: neuromuscular ultrasound for the diagnosis of carpal tunnel syndrome. Muscle Nerve. 2012;46(2):287\u201393. https:\/\/doi.org\/10.1002\/mus.23389.","journal-title":"Muscle Nerve"},{"key":"2729_CR7","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/s10067-020-05293-z","volume":"40","author":"G Smerilli","year":"2021","unstructured":"Smerilli G, Di Matteo A, Cipolletta E, Carloni S, Incorvaia A, Di Carlo M, et al. Ultrasound assessment of carpal tunnel in rheumatoid arthritis and idiopathic carpal tunnel syndrome. Clin Rheumatol. 2021;40:1085\u201392. https:\/\/doi.org\/10.1007\/s10067-020-05293-z.","journal-title":"Clin Rheumatol"},{"issue":"6","key":"2729_CR8","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1016\/j.jbspin.2020.04.018","volume":"87","author":"G Smerilli","year":"2020","unstructured":"Smerilli G, Di Battista J, Cipolletta E, Grassi W, Filippucci E. Additional value of ultrasound in the assessment of carpal tunnel syndrome in rheumatological daily practice. A case of persistent median artery thrombosis. Joint Bone Spine. 2020;87(6):666\u20137. https:\/\/doi.org\/10.1016\/j.jbspin.2020.04.018.","journal-title":"Joint Bone Spine"},{"key":"2729_CR9","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/s1521-6942(02)00099-2","volume":"17","author":"W Grassi","year":"2003","unstructured":"Grassi W, Filippucci E, Carotti M, Salaffi F. Imaging modalities for identifying the origin of regional musculoskeletal pain. Best Pract Res Clin Rheumatol. 2003;17:17\u201332. https:\/\/doi.org\/10.1016\/s1521-6942(02)00099-2.","journal-title":"Best Pract Res Clin Rheumatol"},{"key":"2729_CR10","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1016\/j.ultrasmedbio.2011.06.011","volume":"37","author":"SC Roll","year":"2011","unstructured":"Roll SC, Case-Smith J, Evans KD. Diagnostic accuracy of ultrasonography VS. electromyography in carpal tunnel syndrome: a systematic review of literature. Ultrasound Med Biol. 2011;37:1539\u201353. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2011.06.011.","journal-title":"Ultrasound Med Biol"},{"key":"2729_CR11","first-page":"118","volume":"24","author":"E Filippucci","year":"2006","unstructured":"Filippucci E, Iagnocco A, Meenagh G, Riente L, Delle Sedie A, Bombardieri S, et al. Ultrasound imaging for the rheumatologist II. Ultrasonography of the hand and wrist. Clin Exp Rheumatol. 2006;24:118\u201322.","journal-title":"Clin Exp Rheumatol"},{"key":"2729_CR12","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s10165-008-0046-z","volume":"18","author":"A Delle Sedie","year":"2008","unstructured":"Delle Sedie A, Riente L, Bombardieri S. Limits and perspectives of ultrasound in the diagnosis and management of rheumatic diseases. Mod Rheumatol. 2008;18:125\u201331. https:\/\/doi.org\/10.1007\/s10165-008-0046-z.","journal-title":"Mod Rheumatol"},{"key":"2729_CR13","doi-asserted-by":"publisher","first-page":"e001063","DOI":"10.1136\/rmdopen-2019-001063","volume":"6","author":"B Stoel","year":"2020","unstructured":"Stoel B. Use of artificial intelligence in imaging in rheumatology-current status and future perspectives. RMD Open. 2020;6:e001063. https:\/\/doi.org\/10.1136\/rmdopen-2019-001063.","journal-title":"RMD Open"},{"key":"2729_CR14","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.jacr.2019.06.004","volume":"16","author":"Z Akkus","year":"2019","unstructured":"Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston AD, Philbrick KA, et al. A survey of deep-learning applications in ultrasound: artificial intelligence\u2013powered ultrasound for improving clinical workflow. J Am Coll Radiol. 2019;16:1318\u201328. https:\/\/doi.org\/10.1016\/j.jacr.2019.06.004.","journal-title":"J Am Coll Radiol"},{"key":"2729_CR15","doi-asserted-by":"publisher","first-page":"589197","DOI":"10.3389\/fmed.2021.589197","volume":"8","author":"E Cipolletta","year":"2021","unstructured":"Cipolletta E, Fiorentino MC, Moccia S, Guidotti I, Grassi W, Filippucci E, et al. Artificial Intelligence for Ultrasound Informative Image Selection of Metacarpal Head Cartilage. A Pilot Study. Front Med (Lausanne). 2021;8:589197. https:\/\/doi.org\/10.3389\/fmed.2021.589197.","journal-title":"Front Med (Lausanne)"},{"key":"2729_CR16","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.cmpb.2018.02.001","volume":"158","author":"S Moccia","year":"2018","unstructured":"Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms \u2014 review of methods, datasets and evaluation metrics. Comput Methods Prog Biomed. 2018;158:71\u201391. https:\/\/doi.org\/10.1016\/j.cmpb.2018.02.001.","journal-title":"Comput Methods Prog Biomed"},{"key":"2729_CR17","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.1007\/s10439-020-02553-6","volume":"48","author":"P Zaffino","year":"2020","unstructured":"Zaffino P, Moccia S, De Momi E, Spadea MF. A review on advances in intra-operative imaging for surgery and therapy: imagining the operating room of the future. Ann Biomed Eng. 2020;48:2171\u201391. https:\/\/doi.org\/10.1007\/s10439-020-02553-6.","journal-title":"Ann Biomed Eng"},{"key":"2729_CR18","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1016\/j.ultrasmedbio.2016.05.016","volume":"42","author":"RF Chang","year":"2016","unstructured":"Chang RF, Lee CC, Lo CM. Computer-aided diagnosis of different rotator cuff lesions using shoulder musculoskeletal ultrasound. Ultrasound Med Biol. 2016;42:2315\u201322. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2016.05.016.","journal-title":"Ultrasound Med Biol"},{"issue":"Suppl 2","key":"2729_CR19","doi-asserted-by":"publisher","first-page":"E31","DOI":"10.1055\/s-0031-1281671","volume":"32","author":"AS Klauser","year":"2011","unstructured":"Klauser AS, Franz M, Bellmann Weiler R, Gruber J, Hartig F, Mur E, et al. Contrast-enhanced ultrasonography for the detection of joint vascularity in arthritis - subjective grading versus computer-aided objective quantification. Ultraschall Med. 2011;32(Suppl 2):E31\u20137. https:\/\/doi.org\/10.1055\/s-0031-1281671.","journal-title":"Ultraschall Med"},{"key":"2729_CR20","doi-asserted-by":"publisher","first-page":"e000891","DOI":"10.1136\/rmdopen-2018-000891","volume":"5","author":"JKH Andersen","year":"2019","unstructured":"Andersen JKH, Pedersen JS, Laursen MS, Holtz K, Grauslund J, Savarimuthu TR, et al. Neural networks for automatic scoring of arthritis disease activity on ultrasound images. RMD Open. 2019;5:e000891. https:\/\/doi.org\/10.1136\/rmdopen-2018-000891.","journal-title":"RMD Open"},{"key":"2729_CR21","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1136\/annrheumdis-2019-216636","volume":"79","author":"ABH Christensen","year":"2020","unstructured":"Christensen ABH, Just SA, Andersen JKH, Savarimuthu TR. Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients. Ann Rheum Dis. 2020;79:1189\u201393. https:\/\/doi.org\/10.1136\/annrheumdis-2019-216636 Epub 2020 Jun 5.","journal-title":"Ann Rheum Dis"},{"issue":"Suppl 124","key":"2729_CR22","first-page":"120","volume":"38","author":"C Roncato","year":"2020","unstructured":"Roncato C, Perez L, Brochet-Gu\u00e9gan A, Allix-B\u00e9guec C, Raimbeau A, Gautier G, et al. Colour Doppler ultrasound of temporal arteries for the diagnosis of giant cell arteritis: a multicentre deep learning study. Clin Exp Rheumatol. 2020;38(Suppl 124):120\u20135.","journal-title":"Clin Exp Rheumatol"},{"key":"2729_CR23","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1016\/j.ultrasmedbio.2020.03.017","volume":"46","author":"MH Horng","year":"2020","unstructured":"Horng MH, Yang CW, Sun YN, Yang TH. DeepNerve: a new convolutional neural network for the localization and segmentation of the median nerve in ultrasound image sequences. Ultrasound Med Biol. 2020;46:2439\u201352. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2020.03.017.","journal-title":"Ultrasound Med Biol"},{"key":"2729_CR24","doi-asserted-by":"publisher","first-page":"101687","DOI":"10.1016\/j.compmedimag.2019.101687","volume":"80","author":"YW Wang","year":"2020","unstructured":"Wang YW, Chang RF, Horng YS, Chen CJ. MNT-DeepSL: median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions. Comput Med Imaging Graph. 2020;80:101687. https:\/\/doi.org\/10.1016\/j.compmedimag.2019.101687.","journal-title":"Comput Med Imaging Graph"},{"key":"2729_CR25","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","volume":"5","author":"S Liu","year":"2019","unstructured":"Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, et al. Deep learning in medical ultrasound analysis: a review. Engineering. 2019;5:261\u201375. https:\/\/doi.org\/10.1016\/j.eng.2018.11.020.","journal-title":"Engineering"},{"key":"2729_CR26","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/s13075-021-02484-0","volume":"23","author":"KK Bressem","year":"2021","unstructured":"Bressem KK, Vahldiek JL, Adams L, Niehues SM, Haibel H, Rios Rodriguez V, et al. Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance. Arthritis Res Ther. 2021;23:106. https:\/\/doi.org\/10.1186\/s13075-021-02484-0.","journal-title":"Arthritis Res Ther"},{"key":"2729_CR27","doi-asserted-by":"publisher","first-page":"2980","DOI":"10.1109\/ICCV.2017.322","volume-title":"IEEE International Conference on Computer Vision (ICCV)","author":"K He","year":"2017","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick RB, Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), vol. 2017; 2017. p. 2980\u20138."},{"key":"2729_CR28","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1136\/annrheumdis-2017-211585","volume":"76","author":"I M\u00f6ller","year":"2017","unstructured":"M\u00f6ller I, Janta I, Backhaus M, Ohrndorf S, Bong DA, Martinoli C, et al. The 2017 EULAR standardised procedures for ultrasound imaging in rheumatology. Ann Rheum Dis. 2017;76:1974\u20139. https:\/\/doi.org\/10.1136\/annrheumdis-2017-211585.","journal-title":"Ann Rheum Dis"},{"key":"2729_CR29","first-page":"770","volume":"2016","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE Confer Comput Vision Pattern Recogn (CVPR). 2016;2016:770\u20138.","journal-title":"IEEE Confer Comput Vision Pattern Recogn (CVPR)"},{"key":"2729_CR30","first-page":"936","volume":"2017","author":"T Lin","year":"2017","unstructured":"Lin T, Doll\u00e1r P, Girshick RB, He K, Hariharan B, Belongie SJ. Feature pyramid networks for object detection. IEEE Conference Comput Vision Pattern Recogn (CVPR). 2017;2017:936\u201344.","journal-title":"IEEE Conference Comput Vision Pattern Recogn (CVPR)."},{"key":"2729_CR31","first-page":"740","volume-title":"Microsoft coco: Common objects in context. In European Conference on Computer Vision","author":"T Lin","year":"2014","unstructured":"Lin T, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al. Microsoft coco: Common objects in context. In European Conference on Computer Vision. Cham: Springer; 2014. p. 740\u201355."},{"issue":"1 Suppl 80","key":"2729_CR32","first-page":"S3","volume":"32","author":"W Grassi","year":"2014","unstructured":"Grassi W, Filippucci E. A brief history of ultrasound in rheumatology: where we were. Clin Exp Rheumatol. 2014;32(1 Suppl 80):S3\u20136.","journal-title":"Clin Exp Rheumatol"},{"key":"2729_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13075-019-2050-4","volume":"22","author":"GS Kaeley","year":"2020","unstructured":"Kaeley GS, Bakewell C, Deodhar A. The importance of ultrasound in identifying and differentiating patients with early inflammatory arthritis: a narrative review. Arthritis Res Ther. 2020;22:1. https:\/\/doi.org\/10.1186\/s13075-019-2050-4.","journal-title":"Arthritis Res Ther"},{"key":"2729_CR34","doi-asserted-by":"crossref","first-page":"413","DOI":"10.55563\/clinexprheumatol\/5tf1kq","volume":"39","author":"G Sakellariou","year":"2021","unstructured":"Sakellariou G, Giovannini I, Grignaschi S, Zabotti A, Iagnocco A. New developments in ultrasound in rheumatology: innovative tools and promising applications. Clin Exp Rheumatol. 2021;39:413\u201321.","journal-title":"Clin Exp Rheumatol"},{"key":"2729_CR35","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1148\/radiology.197.1.7568840","volume":"197","author":"E Silvestri","year":"1995","unstructured":"Silvestri E, Martinoli C, Derchi LE, Bertolotto M, Chiaramondia M, Rosenberg I. Echotexture of peripheral nerves: correlation between US and histologic findings and criteria to differentiate tendons. Radiology. 1995;197:291\u20136. https:\/\/doi.org\/10.1148\/radiology.197.1.7568840.","journal-title":"Radiology"},{"key":"2729_CR36","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/s10072-011-0582-8","volume":"32","author":"G Granata","year":"2011","unstructured":"Granata G, Caliandro P, Pazzaglia C, Minciotti I, Russo G, Martinoli C. Prevalence of bifid median nerve at wrist assessed through ultrasound. Neurol Sci. 2011;32:615\u20138. https:\/\/doi.org\/10.1007\/s10072-011-0582-8.","journal-title":"Neurol Sci"}],"container-title":["Arthritis Research &amp; Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13075-022-02729-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13075-022-02729-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13075-022-02729-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T02:20:46Z","timestamp":1674699646000},"score":1,"resource":{"primary":{"URL":"https:\/\/arthritis-research.biomedcentral.com\/articles\/10.1186\/s13075-022-02729-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,8]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["2729"],"URL":"https:\/\/doi.org\/10.1186\/s13075-022-02729-6","relation":{},"ISSN":["1478-6362"],"issn-type":[{"value":"1478-6362","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,8]]},"assertion":[{"value":"5 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted in accordance with the Helsinki Declaration and was approved by the local ethics committee (Comitato Etico Regione Marche, number 262). All patients signed informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"E.F. has received speaking fees from Abbvie, BMS, Janssen, Lilly, MSD, Novartis, Roche, Pfizer, and UCB Pharma. W.G. has received speaking fees from AbbVie, Celgene, Gr\u00fcnenthal, Pfizer, and UCB Pharma. All other authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"38"}}