{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T23:24:14Z","timestamp":1791588254852,"version":"4.3.4"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100014584","name":"Universit\u00e4tsmedizin der Johannes Gutenberg-Universit\u00e4t Mainz","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014584","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Updates Surg"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The use of artificial intelligence (AI) for TI-RADS classification in neck sonography for the assessment of thyroid nodules has been proven beneficial. We evaluated the learning curve of such AI in a real-world setting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Between 03\/2023 and 06\/2024, 110 patients with 176 thyroid nodules were examined and classified according to ACR TI-RADS classification using 3D-ultrasound PIUR tUS Infinity software. After software training and update, the study was repeated examining 133 patients with 228 nodules (03\/2023 until 10\/2024). Every AI-based TI-RADS evaluation was compared to that of an experienced endocrine surgeon (assessor), unaware of the software results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>First phase: AI-supported TI-RADS classification corresponded to the assessor\u2019s in 128\/176 (73%) of examined nodules; second phase: correspondence in 210\/227 (92.6%). Re-evaluating the initial 110 patients after software update, more nodules (194 vs. 176) were correctly assessed. In the first phase, AI \u201cmisinterpreted\u201d in 36\/176 (20%) cases nodules with microcalcifications or echogenic foci (leading to 1\u20133 points differences to accessor). After update, only 11\/227 (4.8%) such nodules remained and relevant differences to the accessor were found in only 6\/227 (2.6%) unusual cases (autoimmune thyroiditis, non-descended thymus, hemorrhaged cyst), compared to the initial 7%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>After several rounds of deep learning, a significant improvement in correct ACR TI-RADS classification was demonstrated, especially the assessment of nodules within conglomerates. The AI-supported ultrasound is already a solid tool in the diagnosis of true thyroid nodules in non-inflamed tissue, but cannot yet replace an experienced clinician in complex or unusual cases; however, the fast learning curve is encouraging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s13304-025-02506-5","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T15:44:00Z","timestamp":1770824640000},"page":"805-816","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Training the diagnostic artificial intelligence in thyroid sonography: how well is deep learning truly learning?"],"prefix":"10.1007","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3700-5557","authenticated-orcid":false,"given":"Oana","family":"Lozan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3398-7283","authenticated-orcid":false,"given":"Petra B.","family":"Musholt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7984-9530","authenticated-orcid":false,"given":"Ann-Kathrin","family":"Lederer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6564-9535","authenticated-orcid":false,"given":"Nabila","family":"Bouzakri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9303-2148","authenticated-orcid":false,"given":"Hauke","family":"Lang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4498-7439","authenticated-orcid":false,"given":"Thomas J.","family":"Musholt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"2506_CR1","doi-asserted-by":"publisher","unstructured":"Madariaga AG, Palacios SS, Guill\u00e9n-Grima F, Galofr\u00e9 JC The Incidence and Prevalence of Thyroid Dysfunction in Europe: A Meta-Analysis, The Journal of Clinical Endocrinology & Metabolism, Volume 99, Issue 3, 1 March 2014, Pages 923\u2013931. https:\/\/doi.org\/10.1210\/jc.2013-2409","DOI":"10.1210\/jc.2013-2409"},{"key":"2506_CR2","doi-asserted-by":"publisher","unstructured":"Diana S, Dean H, Gharib (2008) Epidemiology of thyroid nodules, Best Practice & Research Clinical Endocrinology & Metabolism, Volume 22, Issue 6, Pages 901\u2013911, ISSN 1521-690X. https:\/\/doi.org\/10.1016\/j.beem.2008.09.019","DOI":"10.1016\/j.beem.2008.09.019"},{"issue":"1","key":"2506_CR3","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1089\/thy.2012.0392","volume":"23","author":"N Li","year":"2013","unstructured":"Li N, Du XL, Reitzel LR, Xu L, Sturgis EM (2013) Impact of enhanced detection on the increase in thyroid cancer incidence in the united states: review of incidence trends by socioeconomic status within the surveillance, epidemiology, and end results registry, 1980\u20132008. 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O.L. has received a course travel reimbursement from PIUR imaging.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Patient consent or Ethics approval were not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}