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The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed\n                      <jats:italic>post hoc<\/jats:italic>\n                      using an AI deep learning software marking pulmonary nodules\u2009\u2265\u20096\u2009mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35\/870 patients or AI marks in 458\/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      During the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18\/59 (30.5%) nodules were scored as true positive while 104\/1,862 (5.6%) AI marks in 84\/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104\n                      <jats:italic>versus<\/jats:italic>\n                      18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758\n                      <jats:italic>versus<\/jats:italic>\n                      41) false positives. There was a median number of 1 (IQR: 0\u20132) AI mark per ULDCT.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The use of AI on ULDCT in patients suspected of pulmonary disease in an emergency setting results in the detection of many more incidental pulmonary nodules requiring follow-up (5.8\u00d7) with a high trade-off in terms of false positives (42.9\u00d7).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>AI aids in the detection of incidental pulmonary nodules that require follow-up at chest-CT, aiding early pulmonary cancer detection but also results in an increase of false positive results that are mainly clustered in patients with major abnormalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration<\/jats:title>\n                    <jats:p>The OPTIMACT trial was registered on 6 December 2016 in the National Trial Register (number NTR6163) (onderzoekmetmensen.nl).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>An AI deep learning algorithm was tested on 870 ULDCT examinations acquired in the ED.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>AI detected 5.8 times more pulmonary nodules requiring follow-up (true positives).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>AI resulted in the detection of 42.9 times more false positive results, clustered in patients with major abnormalities.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>AI in the ED setting may aid in early pulmonary cancer detection with a high trade-off in terms of false positives.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-024-00518-1","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T04:39:20Z","timestamp":1732077560000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study"],"prefix":"10.1186","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8724-6502","authenticated-orcid":false,"given":"Inge A. 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The Ethics Committee of the Amsterdam UMC location AMC waived additional approval of this secondary analysis of the OPTIMACT data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The host institution of CJ, Radboud University Medical Center, has received research grants and royalties from MeVis Medical Solutions, Bremen, Germany. JS is a member of the Scientific Editorial Board for <i>European Radiology Experimental<\/i> (section: abdomen\/gastrointestinal). They did not participate in the selection nor review processes for this article. All other authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"132"}}