{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T11:00:44Z","timestamp":1794222044076,"version":"4.8.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["RS-2023-00269924"],"award-info":[{"award-number":["RS-2023-00269924"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-82775-z","type":"journal-article","created":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T08:26:32Z","timestamp":1735374392000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points"],"prefix":"10.1038","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7462-2609","authenticated-orcid":false,"given":"Hyun Joo","family":"Shin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5687-7237","authenticated-orcid":false,"given":"Kyunghwa","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6192-8852","authenticated-orcid":false,"given":"Nak-Hoon","family":"Son","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3368-5013","authenticated-orcid":false,"given":"Eun-Kyung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5634-9709","authenticated-orcid":false,"given":"Min Jung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-4967","authenticated-orcid":false,"given":"Sergios","family":"Gatidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1999-6595","authenticated-orcid":false,"given":"Shreyas","family":"Vasanawala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"82775_CR1","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1016\/j.jacr.2023.06.003","volume":"20","author":"MBK Sammer","year":"2023","unstructured":"Sammer, M. B. K. et al. Use of Artificial Intelligence in Radiology: Impact on Pediatric patients, a White Paper from the ACR Pediatric AI Workgroup. J. Am. Coll. Radiology: JACR 20, 730\u2013737. https:\/\/doi.org\/10.1016\/j.jacr.2023.06.003 (2023).","journal-title":"J. Am. Coll. Radiology: JACR"},{"key":"82775_CR2","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.jacr.2023.04.017","volume":"20","author":"LO Tierradentro-Garcia","year":"2023","unstructured":"Tierradentro-Garcia, L. O., Sotardi, S. T., Sammer, M. B. K. & Otero, H. J. Commercially available Artificial Intelligence algorithms of interest to Pediatric Radiology: the growing gap between potential Use and Data Training. J. Am. Coll. Radiology: JACR 20, 748\u2013751. https:\/\/doi.org\/10.1016\/j.jacr.2023.04.017 (2023).","journal-title":"J. Am. Coll. Radiology: JACR"},{"key":"82775_CR3","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s00247-021-05072-1","volume":"52","author":"MM Moore","year":"2022","unstructured":"Moore, M. M., Iyer, R. S., Sarwani, N. I. & Sze, R. W. Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults. Pediatr. Radiol. 52, 367\u2013373. https:\/\/doi.org\/10.1007\/s00247-021-05072-1 (2022).","journal-title":"Pediatr. Radiol."},{"key":"82775_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2023.04.013","author":"D Alkhulaifat","year":"2023","unstructured":"Alkhulaifat, D., Rafful, P., Khalkhali, V., Welsh, M. & Sotardi, S. Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development. J. Am. Coll. Radiology: JACR. https:\/\/doi.org\/10.1016\/j.jacr.2023.04.013 (2023).","journal-title":"J. Am. Coll. Radiology: JACR"},{"key":"82775_CR5","doi-asserted-by":"publisher","unstructured":"van Leeuwen, K. G., de Rooij, M., Schalekamp, S., van Ginneken, B. & Rutten, M. How does artificial intelligence in radiology improve efficiency and health outcomes? Pediatr. Radiol. (2022) 52, 2087\u20132093. https:\/\/doi.org\/10.1007\/s00247-021-05114-8 (2021).","DOI":"10.1007\/s00247-021-05114-8"},{"key":"82775_CR6","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s00247-021-05195-5","volume":"52","author":"SC Shelmerdine","year":"2022","unstructured":"Shelmerdine, S. C., Rosendahl, K. & Arthurs, O. J. Artificial intelligence in paediatric radiology: international survey of health care professionals\u2019 opinions. Pediatr. Radiol. 52, 30\u201341. https:\/\/doi.org\/10.1007\/s00247-021-05195-5 (2022).","journal-title":"Pediatr. Radiol."},{"key":"82775_CR7","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/s41746-023-00829-4","volume":"6","author":"HJ Shin","year":"2023","unstructured":"Shin, H. J., Han, K., Ryu, L. & Kim, E. K. The impact of artificial intelligence on the reading times of radiologists for chest radiographs. NPJ Digit. Med. 6, 82. https:\/\/doi.org\/10.1038\/s41746-023-00829-4 (2023).","journal-title":"NPJ Digit. Med."},{"key":"82775_CR8","doi-asserted-by":"publisher","first-page":"19732","DOI":"10.1038\/s41598-023-47194-6","volume":"13","author":"SH Hwang","year":"2023","unstructured":"Hwang, S. H., Shin, H. J., Kim, E. K., Lee, E. H. & Lee, M. Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence. Sci. Rep. 13, 19732. https:\/\/doi.org\/10.1038\/s41598-023-47194-6 (2023).","journal-title":"Sci. Rep."},{"key":"82775_CR9","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2022.0193","author":"S Lee","year":"2022","unstructured":"Lee, S., Shin, H. J., Kim, S. & Kim, E. K. Successful implementation of an Artificial Intelligence-based computer-aided detection system for chest radiography in Daily Clinical Practice. Korean J. Radiol. https:\/\/doi.org\/10.3348\/kjr.2022.0193 (2022).","journal-title":"Korean J. Radiol."},{"key":"82775_CR10","doi-asserted-by":"publisher","first-page":"e0282123","DOI":"10.1371\/journal.pone.0282123","volume":"18","author":"HJ Shin","year":"2023","unstructured":"Shin, H. J., Lee, S., Kim, S., Son, N. H. & Kim, E. K. Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs. PloS One 18, e0282123. https:\/\/doi.org\/10.1371\/journal.pone.0282123 (2023).","journal-title":"PloS One"},{"key":"82775_CR11","doi-asserted-by":"publisher","first-page":"19420","DOI":"10.1038\/s41598-019-55536-6","volume":"9","author":"S Kim","year":"2019","unstructured":"Kim, S. et al. Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children. Sci. Rep. 9, 19420. https:\/\/doi.org\/10.1038\/s41598-019-55536-6 (2019).","journal-title":"Sci. Rep."},{"key":"82775_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00247-021-05057-0","author":"JR Dillman","year":"2021","unstructured":"Dillman, J. R., Somasundaram, E., Brady, S. L. & He, L. Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr. Radiol. https:\/\/doi.org\/10.1007\/s00247-021-05057-0 (2021).","journal-title":"Pediatr. Radiol."},{"key":"82775_CR13","doi-asserted-by":"publisher","first-page":"343","DOI":"10.3348\/kjr.2021.0449","volume":"23","author":"JW Choi","year":"2022","unstructured":"Choi, J. W. et al. Deep learning-assisted diagnosis of Pediatric Skull fractures on plain radiographs. Korean J. Radiol. 23, 343\u2013354. https:\/\/doi.org\/10.3348\/kjr.2021.0449 (2022).","journal-title":"Korean J. Radiol."},{"key":"82775_CR14","doi-asserted-by":"publisher","first-page":"10215","DOI":"10.1038\/s41598-022-14519-w","volume":"12","author":"HJ Shin","year":"2022","unstructured":"Shin, H. J., Son, N. H., Kim, M. J. & Kim, E. K. Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs. Sci. Rep. 12, 10215. https:\/\/doi.org\/10.1038\/s41598-022-14519-w (2022).","journal-title":"Sci. Rep."},{"key":"82775_CR15","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.jacr.2023.07.004","volume":"20","author":"G Morcos","year":"2023","unstructured":"Morcos, G., Yi, P. H. & Jeudy, J. Applying Artificial Intelligence to Pediatric chest imaging: reliability of leveraging adult-based Artificial Intelligence models. J. Am. Coll. Radiology: JACR 20, 742\u2013747. https:\/\/doi.org\/10.1016\/j.jacr.2023.07.004 (2023).","journal-title":"J. Am. Coll. Radiology: JACR"},{"key":"82775_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00247-023-05746-y","author":"P Ciet","year":"2023","unstructured":"Ciet, P. et al. The unintended consequences of artificial intelligence in paediatric radiology. Pediatr. Radiol. https:\/\/doi.org\/10.1007\/s00247-023-05746-y (2023).","journal-title":"Pediatr. Radiol."},{"key":"82775_CR17","doi-asserted-by":"publisher","first-page":"19624","DOI":"10.1038\/s41598-024-70780-1","volume":"14","author":"S Lee","year":"2024","unstructured":"Lee, S. et al. Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence. Sci. Rep. 14, 19624. https:\/\/doi.org\/10.1038\/s41598-024-70780-1 (2024).","journal-title":"Sci. Rep."},{"key":"82775_CR18","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1148\/radiol.2018180237","volume":"290","author":"JG Nam","year":"2019","unstructured":"Nam, J. G. et al. Development and Validation of Deep Learning-based Automatic Detection Algorithm for malignant pulmonary nodules on chest radiographs. Radiology 290, 218\u2013228. https:\/\/doi.org\/10.1148\/radiol.2018180237 (2019).","journal-title":"Radiology"},{"key":"82775_CR19","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.3348\/kjr.2021.0544","volume":"22","author":"EJ Hwang","year":"2021","unstructured":"Hwang, E. J. et al. Use of Artificial Intelligence-Based Software as Medical devices for chest radiography: a position paper from the Korean Society of Thoracic Radiology. Korean J. Radiol. 22, 1743\u20131748. https:\/\/doi.org\/10.3348\/kjr.2021.0544 (2021).","journal-title":"Korean J. Radiol."},{"key":"82775_CR20","doi-asserted-by":"publisher","first-page":"e0264383","DOI":"10.1371\/journal.pone.0264383","volume":"17","author":"EY Kim","year":"2022","unstructured":"Kim, E. Y. et al. Concordance rate of radiologists and a commercialized deep-learning solution for chest X-ray: real-world experience with a multicenter health screening cohort. PloS One 17, e0264383. https:\/\/doi.org\/10.1371\/journal.pone.0264383 (2022).","journal-title":"PloS One"},{"key":"82775_CR21","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1007\/s00247-022-05368-w","volume":"52","author":"S Padash","year":"2022","unstructured":"Padash, S., Mohebbian, M. R., Adams, S. J., Henderson, R. D. E. & Babyn, P. Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review. Pediatr. Radiol. 52, 1568\u20131580. https:\/\/doi.org\/10.1007\/s00247-022-05368-w (2022).","journal-title":"Pediatr. Radiol."},{"key":"82775_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08397-5","author":"KN Jin","year":"2022","unstructured":"Jin, K. N. et al. Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study. Eur. Radiol. https:\/\/doi.org\/10.1007\/s00330-021-08397-5 (2022).","journal-title":"Eur. Radiol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-82775-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-82775-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-82775-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T21:29:13Z","timestamp":1735421353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-82775-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,28]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["82775"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-82775-z","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,28]]},"assertion":[{"value":"30 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"31329"}}