{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T09:04:07Z","timestamp":1780563847822,"version":"3.54.1"},"reference-count":18,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Bethyl Musculoskeletal Research Center"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hand (New York, N,Y.)"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>In this observational cohort study, patients \u226518 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15589447261447514","type":"journal-article","created":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T06:52:21Z","timestamp":1780210341000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["An AI-Driven Pipeline for Localization, Segmentation, and Classification of Carpal Tunnel Syndrome Using Ultrasound Images of the Median Nerve"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8194-6361","authenticated-orcid":false,"given":"Rachel","family":"Hyzny","sequence":"first","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles J.","family":"Patterson","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhigyan","family":"Kishor","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nickolas","family":"Littlefield","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chinemerem","family":"Nwosu","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Weinberg","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad P.","family":"Tafti","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John R.","family":"Fowler","sequence":"additional","affiliation":[{"name":"University of Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.282.2.153"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/occmed\/kql125"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11999-010-1637-5"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/1558944717731857"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2011.06.011"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhsa.2006.01.020"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TUFFC.2023.3330539"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00264-025-06497-1"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"e_1_3_3_11_2","first-page":"424","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI","author":"\u00c7i\u00e7ek Abdulkadir \u00d6A","unstructured":"\u00c7i\u00e7ek Abdulkadir \u00d6A, Lienkamp SS, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W, eds. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI. Springer; 2016:424-432."},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(22)00432-X"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.5435\/JAAOS-D-24-01179"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1002\/jum.16486"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12891-023-06623-3"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12030632"}],"container-title":["HAND"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15589447261447514","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15589447261447514","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15589447261447514","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T08:18:32Z","timestamp":1780561112000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15589447261447514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,30]]},"references-count":18,"alternative-id":["10.1177\/15589447261447514"],"URL":"https:\/\/doi.org\/10.1177\/15589447261447514","relation":{},"ISSN":["1558-9447","1558-9455"],"issn-type":[{"value":"1558-9447","type":"print"},{"value":"1558-9455","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,30]]},"article-number":"15589447261447514"}}