{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T23:24:12Z","timestamp":1791588252903,"version":"4.3.4"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:00:00Z","timestamp":1749081600000},"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":["Endocrine"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Thyroid nodules are commonly evaluated using ultrasound-based risk stratification systems, which rely on subjective descriptors. Artificial intelligence (AI) may improve assessment, but its effectiveness in non-subspecialist settings is unclear. This study evaluated the impact of an AI-based decision support system (AI-DSS) on thyroid nodule ultrasound assessments by general endocrinologists (GE) without subspecialty thyroid imaging training.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A prospective cohort study was conducted on 80 patients undergoing thyroid ultrasound in GE outpatient clinics. Thyroid ultrasound was performed based on clinical judgment as part of routine care by GE. Images were retrospectively analyzed using an AI-DSS (Koios DS), independently of clinician assessments. AI-DSS results were compared with initial GE evaluations and, when referred, with expert evaluations at a subspecialized thyroid nodule clinic (TNC). Agreement in ultrasound features, risk classification by the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) and American Thyroid Association guidelines, and referral recommendations was assessed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI-DSS differed notably from GE, particularly assessing nodule composition (solid: 80%vs.36%,p\u2009&lt;\u20090.01), echogenicity (hypoechoic:52%vs.16%,p\u2009&lt;\u20090.01), and echogenic foci (microcalcifications:10.7%vs.1.3%,p\u2009&lt;\u20090.05). AI-DSS classification led to a higher referral rate compared to GE (37.3%vs.30.7%, not statistically significant). Agreement between AI-DSS and GE in ACR TI-RADS scoring was moderate (r\u2009=\u20090.337;p\u2009&lt;\u20090.001), but improved when comparing GE to AI-DSS and TNC subspecialist (r\u2009=\u20090.465;p\u2009&lt;\u20090.05 and r\u2009=\u20090.607;p\u2009&lt;\u20090.05, respectively).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In a non-subspecialist setting, non-adjunct AI-DSS use did not significantly improve risk stratification or reduce hypothetical referrals. The system tended to overestimate risk, potentially leading to unnecessary procedures. Further optimization is required for AI to function effectively in low-prevalence environment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s12020-025-04287-8","type":"journal-article","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T01:37:35Z","timestamp":1749087455000},"page":"817-825","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Preliminary analysis of AI-based thyroid nodule evaluation in a non-subspecialist endocrinology setting"],"prefix":"10.1007","volume":"89","author":[{"given":"Pablo","family":"Fern\u00e1ndez Velasco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucia","family":"Est\u00e9vez Asensio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beatriz","family":"Torres","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana","family":"Ortol\u00e1","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emilia","family":"G\u00f3mez Hoyos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esther","family":"Delgado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"de Lu\u00eds","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gonzalo","family":"D\u00edaz Soto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"issue":"1","key":"4287_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1089\/thy.2015.0020","volume":"26","author":"BR Haugen","year":"2016","unstructured":"B.R. Haugen, E.K. Alexander, K.C. Bible, G.M. Doherty, S.J. Mandel, Y.E. Nikiforov et al., 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the american thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid 26(1), 1\u2013133 (2016).","journal-title":"Thyroid"},{"issue":"5","key":"4287_CR2","doi-asserted-by":"publisher","first-page":"e230067","DOI":"10.1530\/ETJ-23-0067","volume":"12","author":"C Durante","year":"2023","unstructured":"C. Durante, L. Heged\u00fcs, A. Czarniecka, R. Paschke, G. Russ, F. Schmitt et al., 2023 European Thyroid Association Clinical Practice Guidelines for thyroid nodule management. Eur. Thyroid. J. 12(5), e230067 (2023).","journal-title":"Eur. Thyroid. J."},{"issue":"9","key":"4287_CR3","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1001\/jama.2018.0898","volume":"319","author":"C Durante","year":"2018","unstructured":"C. Durante, G. Grani, L. Lamartina, S. Filetti, S.J. Mandel, D.S. Cooper, The diagnosis and management of thyroid nodules: a review. JAMA 319(9), 914\u2013924 (2018).","journal-title":"JAMA"},{"issue":"5","key":"4287_CR4","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.jacr.2017.01.046","volume":"14","author":"FN Tessler","year":"2017","unstructured":"F.N. Tessler, W.D. Middleton, E.G. Grant, J.K. Hoang, L.L. Berland, S.A. Teefey et al., ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee. J. Am. Coll. Radiol. 14(5), 587\u2013595 (2017).","journal-title":"J. Am. Coll. Radiol."},{"issue":"5","key":"4287_CR5","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.2214\/AJR.17.18822","volume":"210","author":"WD Middleton","year":"2018","unstructured":"W.D. Middleton, S.A. Teefey, C.C. Reading, J.E. Langer, M.D. Beland, M.M. Szabunio et al., Comparison of performance characteristics of American college of radiology TI-RADS, Korean society of thyroid radiology TIRADS, and American thyroid association guidelines. Am. J. Roentgenol. 210(5), 1148\u20131154 (2018).","journal-title":"Am. J. Roentgenol."},{"issue":"2","key":"4287_CR6","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1089\/thy.2019.0360","volume":"30","author":"A Persichetti","year":"2020","unstructured":"A. Persichetti, E. Di Stasio, C. Coccaro, F. Graziano, A. Bianchini, V. Di Donna et al., Inter- and intraobserver agreement in the assessment of thyroid nodule ultrasound features and classification systems: a blinded multicenter study. Thyroid 30(2), 237\u2013242 (2020).","journal-title":"Thyroid"},{"issue":"8","key":"4287_CR7","doi-asserted-by":"publisher","first-page":"e3288","DOI":"10.1210\/clinem\/dgac279","volume":"107","author":"N Burgos","year":"2022","unstructured":"N. Burgos, J. Zhao, J.P. Brito, J.K. Hoang, F. Pitoia, S. Maraka et al., Clinician agreement on the classification of thyroid nodules ultrasound features: a survey of 2 endocrine societies. J. Clin. Endocrinol. Metab. 107(8), e3288\u2013e3294 (2022).","journal-title":"J. Clin. Endocrinol. Metab."},{"issue":"8","key":"4287_CR8","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1089\/thy.2023.0132","volume":"33","author":"D Toro-Tobon","year":"2023","unstructured":"D. Toro-Tobon, R. Loor-Torres, M. Duran, J.W. Fan, N. Singh Ospina, Y. Wu et al., Artificial intelligence in thyroidology: a narrative review of the current applications, associated challenges, and future directions. Thyroid\u00ae 33(8), 903\u2013917 (2023).","journal-title":"Thyroid\u00ae"},{"issue":"4","key":"4287_CR9","doi-asserted-by":"publisher","first-page":"547","DOI":"10.2214\/AJR.22.27430","volume":"219","author":"B Wildman-Tobriner","year":"2022","unstructured":"B. Wildman-Tobriner, E. Taghi-Zadeh, M.A. Mazurowski, Artificial Intelligence (AI) tools for thyroid nodules on ultrasound, from the AJR special series on AI applications. Am. J. Roentgenol. 219(4), 547\u2013554 (2022).","journal-title":"Am. J. Roentgenol."},{"key":"4287_CR10","first-page":"510","volume":"34","author":"P Fern\u00e1ndez Velasco","year":"2024","unstructured":"P. Fern\u00e1ndez Velasco, P. P\u00e9rez L\u00f3pez, B. Torres Torres, E. Delgado, D. De Luis, G. D\u00edaz Soto, Clinical evaluation of an artificial intelligence-based decision support system for the diagnosis and american college of radiology thyroid imaging reporting and data system classification of thyroid nodules. Thyroid\u00ae 34, 510\u2013518 (2024).","journal-title":"Thyroid\u00ae"},{"issue":"10","key":"4287_CR11","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.endonu.2014.05.004","volume":"61","author":"G D\u00edaz-Soto","year":"2014","unstructured":"G. D\u00edaz-Soto, B. Torres, J.J. L\u00f3pez, D. De Luis, Estructura diagn\u00f3stica y funcional de una consulta de alta resoluci\u00f3n de n\u00f3dulo tiroideo: un modelo de eficiencia econ\u00f3mica. Endocrinolog\u00eda y. Nutrici\u00f3n 61(10), 552\u2013553 (2014).","journal-title":"Endocrinolog\u00eda y. Nutrici\u00f3n"},{"issue":"11","key":"4287_CR12","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1089\/thy.2024.0410","volume":"34","author":"J Ashton","year":"2024","unstructured":"J. Ashton, S. Morrison, A. Erkanli, B. Wildman-Tobriner, Assessment of the diagnostic performance of a commercially available artificial intelligence algorithm for risk stratification of thyroid nodules on ultrasound. Thyroid\u00ae 34(11), 1379\u20131388 (2024).","journal-title":"Thyroid\u00ae"},{"key":"4287_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/9492056","volume":"2022","author":"Y Xue","year":"2022","unstructured":"Y. Xue, Y. Zhou, T. Wang, H. Chen, L. Wu, H. Ling et al., Accuracy of ultrasound diagnosis of thyroid nodules based on artificial intelligence-assisted diagnostic technology: a systematic review and meta-analysis. Int. J. Endocrinol. 2022, 1\u201313 (2022). Casella C, editor.","journal-title":"Int. J. Endocrinol."},{"issue":"6","key":"4287_CR14","doi-asserted-by":"publisher","first-page":"2392","DOI":"10.1007\/s10278-023-00884-z","volume":"36","author":"L Barinov","year":"2023","unstructured":"L. Barinov, A. Jairaj, W.D. Middleton, D.M. Beland, J. Kirsch et al., Improving the efficacy of ACR TI-RADS through deep learning-based descriptor augmentation. J. Digit. Imaging 36(6), 2392\u20132401 (2023).","journal-title":"J. Digit. Imaging"},{"key":"4287_CR15","unstructured":"U.S. Food & Drug Administration. 510(k) Summary: Koios DS (K242130) [Internet]. Silver Spring, MD, USA: U.S. Food & Drug Administration; 2024 Nov. 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