{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:12:10Z","timestamp":1791997930252,"version":"4.4.0"},"reference-count":29,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100005595","name":"University of California","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:sec><jats:title>Objective\/Hypothesis<\/jats:title><jats:p>Standard chest radiographs are a poor diagnostic tool for pediatric foreign body aspiration. Machine learning may improve upon the diagnostic capabilities of chest radiographs. The objective is to develop a machine learning algorithm that improves the diagnostic capabilities of chest radiographs in pediatric foreign body aspiration.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>This retrospective, diagnostic study included a retrospective chart review of patients with a potential diagnosis of FBA from 2010 to 2020. Frontal view chest radiographs were extracted, processed, and uploaded to Google AutoML Vision. The developed algorithm was then evaluated against a pediatric radiologist.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The study selected 566 patients who were presented with a suspected diagnosis of foreign body aspiration. One thousand six hundred and eighty eight chest radiograph images were collected. The sensitivity and specificity of the radiologist interpretation were 50.6% (43.1\u201358.0) and 88.7% (85.3\u201391.5), respectively. The sensitivity and specificity of the algorithm were 66.7% (43.0\u201385.4) and 95.3% (90.6\u201398.1), respectively. The precision and recall of the algorithm were both 91.8% with an AuPRC of 98.3%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Chest radiograph analysis augmented with machine learning can diagnose foreign body aspiration in pediatric patients at a level similar to a read performed by a pediatric radiologist despite only using single\u2010view, fixed images. Overall, this study highlights the potential and capabilities of machine learning in diagnosing conditions with a wide range of clinical presentations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3 <jats:italic>Laryngoscope<\/jats:italic>, 134:3807\u20133814, 2024<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.31338","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T03:34:47Z","timestamp":1708140887000},"page":"3807-3814","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated Detection of Pediatric Foreign Body Aspiration from Chest X\u2010rays Using Machine Learning"],"prefix":"10.1002","volume":"134","author":[{"given":"Brandon","family":"Truong","sequence":"first","affiliation":[{"name":"School of Medicine University of California  San Francisco California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Zapala","sequence":"additional","affiliation":[{"name":"Division of Pediatric Radiology, Department of Radiology and Biomedical Imaging University of California  San Francisco California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bamidele","family":"Kammen","sequence":"additional","affiliation":[{"name":"Division of Pediatric Radiology, Department of Radiology and Biomedical Imaging University of California  San Francisco California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2162-7251","authenticated-orcid":false,"given":"Kimberly","family":"Luu","sequence":"additional","affiliation":[{"name":"Division of Pediatric Otolaryngology, Department of Otolaryngology\u2010Head and Neck Surgery University of California  San Francisco California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2,17]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijporl.2012.02.010"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/edpract-2019-318121"},{"key":"e_1_2_6_4_1","volume-title":"StatPearls [Internet]","author":"Cramer N","year":"2020"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.14744\/TurkPediatriArs.2019.60251"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/ppul.20065"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/bf02389562"},{"issue":"4","key":"e_1_2_6_8_1","first-page":"103","article-title":"Diagnostic accuracy of chest x\u2010ray in tracheobronchial foreign body aspiration in paediatric patients","volume":"23","author":"Sattar A","year":"2011","journal-title":"J Ayub Med Coll Abbottabad"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpedsurg.2019.09.045"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijporl.2017.08.036"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10140-020-01764-7"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0022-3468(03)00263-x"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-3468(98)90601-7"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599818800470"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/17476348.2018.1494576"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599819827507"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.27850"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-51503-3"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180237"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-13969-8_18"},{"key":"e_1_2_6_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"e_1_2_6_22_1","doi-asserted-by":"crossref","unstructured":"DavisJ GoadrichM.The relationship between precision\u2010recall and ROC curves. 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