{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:49:19Z","timestamp":1786978159403,"version":"build-2736575974"},"reference-count":32,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Ultrasound is widely used in diagnosing carpal tunnel syndrome (CTS). However, the limitations of ultrasound in CTS detection are the lack of objective measures in the detection of nerve abnormality and the operator\u2010dependent nature of ultrasound imaging. Therefore, in this study, we developed and proposed externally validated artificial intelligence (AI) models based on deep\u2010radiomics features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We have used 416 median nerves from 2 countries (Iran and Colombia) for the development (112 entrapped and 112 normal nerves from Iran) and validation (26 entrapped and 26 normal nerves from Iran, and 70 entrapped and 70 normal nerves from Columbia) of our models. Ultrasound images were fed to the SqueezNet architecture to extract deep\u2010radiomics features. Then a ReliefF method was used to select the clinically significant features. The selected deep\u2010radiomics features were fed to 9 common machine\u2010learning algorithms to choose the best\u2010performing classifier. The 2 best\u2010performing AI models were then externally validated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our developed model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.910 (88.46% sensitivity, 88.46% specificity) and 0.908 (84.62% sensitivity, 88.46% specificity) with support vector machine and stochastic gradient descent (SGD), respectively using the internal validation dataset. Furthermore, both models consistently performed well in the external validation dataset, and achieved an AUC of 0.890 (85.71% sensitivity, 82.86% specificity) and 0.890 (84.29% sensitivity and 82.86% specificity), with SVM and SGD models, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our proposed AI models fed with deep\u2010radiomics features performed consistently with internal and external datasets. This justifies that our proposed system can be employed for clinical use in hospitals and polyclinics.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jum.16244","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T06:38:37Z","timestamp":1682491117000},"page":"2257-2268","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Deep Radiomics Features of Median Nerves for Automated Diagnosis of Carpal Tunnel Syndrome With Ultrasound Images: A Multi\u2010Center Study"],"prefix":"10.1002","volume":"42","author":[{"given":"Afshin","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Department of Radiology, Faculty of Medicine Urmia University of Medical Science  Urmia Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Torres\u2010Cuenca","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation National University of Colombia  Bogot\u00e1 Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Mirza\u2010Aghazadeh\u2010Attari","sequence":"additional","affiliation":[{"name":"Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine Johns Hopkins University  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fariborz","family":"Faeghi","sequence":"additional","affiliation":[{"name":"Department of Radiology Technology, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing University of Southern Queensland  Springfield Queensland Australia"},{"name":"Department of Biomedical Engineering, School of Science and Technology SUSS University  Singapore"},{"name":"Department of Biomedical Informatics and Medical Engineering Asia University  Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-0973","authenticated-orcid":false,"given":"Ali","family":"Abbasian Ardakani","sequence":"additional","affiliation":[{"name":"Department of Radiology Technology, School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences  Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.2174\/1874325001206010069"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/oemed-2014-102534"},{"key":"e_1_2_7_4_1","volume-title":"Electrodiagnostic Evaluation of Carpal Tunnel Syndrome","author":"Rosario NB","year":"2020"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.4055\/cios.2014.6.3.253"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10080596"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2019.01.022"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.02.020"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/229255030901700302"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2022.06.011"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-97043-7"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106750"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.7863\/ultra.15.12014"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mus.23735"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109518"},{"key":"e_1_2_7_16_1","unstructured":"Ch\u00e1vezCDE.Un filtro para la eliminaci\u00f3n de ruido mediante correlaci\u00f3n espacial en im\u00e1genes: Centro de Investigaci\u00f3n en Matem\u00e1ticas.2012."},{"key":"e_1_2_7_17_1","article-title":"SqueezeNet: AlexNet\u2010level accuracy with 50x fewer parameters and <0.5 MB model size","volume":"160207360","author":"Iandola FN","year":"2016","journal-title":"arXiv"},{"key":"e_1_2_7_18_1","doi-asserted-by":"crossref","unstructured":"SzegedyC LiuW JiaY et al.Going deeper with convolutions. 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