{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T20:01:14Z","timestamp":1781640074063,"version":"3.54.5"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004721","name":"The University of Tokyo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Emerg Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate the diagnostic performance of artificial intelligence (AI)-based software for pneumothorax detection on supine radiographs and its impact on physicians\u2019 interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center retrospective study analyzed 114 hemithoraces with pneumothorax and 340 without, using computed tomography as the reference standard. We evaluated the performance of CXR-AID, an AI-based software, for pneumothorax detection on supine chest radiographs, and conducted a reader study to assess the utility of AI assistance. Sensitivities and specificities were adjusted for clustering within patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Sensitivity and specificity of the AI in detecting overall pneumothorax on a supine chest radiograph were 61.0% (95% confidence interval [CI], 50.6%\u201370.4%) and 94.3% (95% CI, 90.8%\u201396.5%), respectively. Sensitivity of the AI in detecting a large pneumothorax with a maximum radial interpleural distance\u2009&gt;\u200935\u00a0mm was 97.4% (95% CI, 83.6%\u201399.6%). Sensitivity was significantly higher in the upper lung zone than in the lower lung zone (69.5% [95% CI, 59.3%\u201378.1%] vs. 37.5% [95% CI, 27.3%\u201348.8%]). In the reader study, the AI significantly improved resident sensitivity (46.8% to 57.3%,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). For experts, the AI did not improve sensitivity significantly (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.32) but significantly improved specificity (90.9% to 95.6%,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.02).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AI demonstrated high sensitivity for detecting large pneumothoraces on supine radiographs, helping identify patients requiring tube thoracotomy. It may serve as a diagnostic safety net for residents by increasing sensitivity and enhances experts\u2019 diagnostic confidence by improving specificity. However, pneumothorax detection in the lower lung zone remains challenging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10140-026-02448-4","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:47:58Z","timestamp":1773190078000},"page":"281-290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance of an artificial intelligence\u2013based software in detecting pneumothorax on supine chest radiographs: a retrospective study"],"prefix":"10.1007","volume":"33","author":[{"given":"Hitomi","family":"Nakamura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoki","family":"Wada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryota","family":"Inokuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouhei","family":"Hanaoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoya","family":"Sakamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kent","family":"Doi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"2448_CR1","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1097\/00003246-199107000-00014","volume":"19","author":"M Kollef","year":"1991","unstructured":"Kollef M (1991) Risk factors for the misdiagnosis of pneumothorax in the intensive care unit. 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