{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:07:29Z","timestamp":1790140049126,"version":"4.0.1"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023QH328"],"award-info":[{"award-number":["ZR2023QH328"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lymphadenopathy may result from inflammation, tuberculosis, or tumors, with lymph node status being a key prognostic indicator. Early accurate differentiation of benign and malignant lesions is therefore vital for clinical management. Although imaging offers adjunctive information, histopathology and cytology on biopsy specimens remain the gold standard, albeit with a 2\u20134 day turnaround. CT-guided biopsy ensures precise sampling, but final diagnosis depends on pathology. AI-ROSE is an emerging real-time cytological tool that can rapidly classify lesions and guide sampling\/treatment decisions. While well-validated in lung biopsy, its role in lymph node biopsy is less studied, as most AI research emphasizes imaging over cytology. Therefore, this study assessed the clinical value and diagnostic concordance of AI-ROSE, providing a reference for intraoperative rapid diagnosis and specimen adequacy evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 54 patients who underwent lymph node biopsy from June 2024 to June 2026. All samples were obtained by percutaneous puncture under CT guidance and simultaneously underwent AI-ROSE analysis, exfoliative cytology examination, and histopathological examination. Using the histopathological results as the gold standard, the sensitivity, specificity, positive\/negative predictive values, and accuracy of AI-ROSE and exfoliative cytology methods were calculated separately, and the consistency between the methods and histopathological diagnosis was analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The sensitivity of the AI-ROSE diagnosis was 90.48% (95% CI: 77.9% \u2013 96.2%), and the diagnostic accuracy was 89.80% (95% CI: 78.2% \u2013 95.6%). Both of these indicators were higher than those of the exfoliative cytology examination. The consistency between AI-ROSE and the pathological gold standard was moderate (\n                      <jats:italic>\u03ba<\/jats:italic>\n                      \u2009=\u20090.647,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this study, AI-ROSE showed higher sensitivity and diagnostic accuracy than traditional exfoliative cytology for lymph node biopsy. As a practical adjunctive tool, it offers real-time guidance on specimen adequacy and the need for repeat puncture, thereby streamlining the diagnostic process. Notably, AI-ROSE findings should be used as supplementary references and must not replace final histopathological diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1960803","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T04:53:59Z","timestamp":1790139239000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence for rapid on-site evaluation of lymph node fine-needle aspiration: improving diagnostic efficiency and accuracy"],"prefix":"10.3389","volume":"13","author":[{"given":"Chengcheng","family":"Du","sequence":"first","affiliation":[{"name":"Department of Radiology, Qilu Hospital of Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhai","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Qilu Hospital of Shandong 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