{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T11:56:42Z","timestamp":1790855802745,"version":"4.1.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["U01 HL089897 and U01 HL089856"],"award-info":[{"award-number":["U01 HL089897 and U01 HL089856"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["75N92023D00011"],"award-info":[{"award-number":["75N92023D00011"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To compare automated mucus plug detection with radiologist evaluation, assess AI-assisted detection, and examine associations of algorithm-derived airway obstruction burden with COPD severity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective analysis included 278 COPDGene participants selected across never-smokers, smokers with preserved ratio-impaired spirometry, and GOLD stages 0\u20134. An AI tool generated candidate plugs. Two thoracic radiologists independently identified plugs in unaided and AI-assisted sessions. A third thoracic radiologist adjudicated all candidates as the reference. Patient- and plug-level performance, observer agreement, false-positive detection, and rank-transformed associations with pulmonary function, CT parameters, and clinical severity were evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Reference mucus plugs were present in 114\/278 participants (41%). Patient-level AI sensitivity was lower than R1 (68.4% vs 86.8%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) but not different from R2 (68.4% vs 79.8%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.141), whereas specificity was similar across approaches (95.7% vs 98.8%\u2013100.0%). Plug-level AI sensitivity was lower than both readers (34.8% vs 58.9% for R1 and 55.6% for R2; both\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). AI assistance improved readers\u2019 plug-level sensitivity (63.6\u201368.6%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2264\u20090.004). AI-reader agreement was good (\u03ba, 0.62\u20130.69), and inter-reader agreement improved with AI (\u03ba, 0.70 vs 0.78;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). These non-reference-positive AI detections (0.396\/scan) were mostly marked bronchial wall thickening or partially occlusive mucus. They were independently associated with worse airflow limitation, CT emphysema, Pi10, and clinical severity after accounting for true-positive burden, improving model fit (\u0394\n                      <jats:italic>R<\/jats:italic>\n                      \u00b2, 0.03\u20130.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI was less sensitive than radiologists but had similar specificity for mucus plug detection. AI assistance improved reader sensitivity and agreement. Additional algorithm-detected airway abnormalities remained associated with airway disease severity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Automated mucus plug detection on chest CT may enable standardized COPD assessment, but independent validation against radiologist assessments remains limited<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Artificial intelligence showed lower sensitivity than radiologists but similar patient-level specificity on chest CT, and assistance improved sensitivity and inter-reader agreement<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Automated mucus plug analysis may support standardized airway obstruction burden assessment in COPD research and clinical trials while retaining radiologist oversight<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12868-y","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T07:42:14Z","timestamp":1790754134000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Radiologist and AI performance in detecting mucus plugs on chest CT"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3700-0165","authenticated-orcid":false,"given":"Soonho","family":"Yoon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seth J.","family":"Kligerman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel R.","family":"Friedlander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R\u016bdolfs","family":"Lati\u0161enko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Paul","family":"Charbonnier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harm","family":"Tiddens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David A.","family":"Lynch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen M.","family":"Humphries","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"12868_CR1","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/S2213-2600(24)00339-4","volume":"13","author":"MM de Oca","year":"2025","unstructured":"de Oca MM, Perez-Padilla R, Celli B et al (2025) The global burden of COPD: epidemiology and effect of prevention strategies. 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S.Y. holds stocks in MEDICAL IP, outside this work. The remaining authors declare no competing interests.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"One of the authors has significant statistical expertise. No complex statistical methods were necessary for this paper.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was obtained from all participants in the COPDGene study.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained at each participating COPDGene site.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Because this project uses COPDGene data, there is potential subject overlap with prior COPDGene publications that have examined mucus plugs and related clinical outcomes. However, the present work is distinct in its specific aim and design: it focuses on independent validation of automated mucus plug detection against an adjudicated radiologist reference standard, direct comparison of AI and radiologist performance at both the patient and plug levels, and evaluation of AI-assisted reader performance.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}