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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Radiolucent foreign body aspiration (FBA) remains diagnostically challenging due to its subtle imaging signatures on chest CT scans, often leading to delayed or missed diagnoses. We present a deep learning model integrating MedpSeg, a high-precision airway segmentation method, with a convolutional classifier to detect radiolucent FBA. The model was trained and validated across three independent cohorts, demonstrating consistent performance with accuracies above 90% and balanced recall\u2013precision metrics. In a blinded independent evaluation cohort, the model outperformed expert radiologists in both recall (71.4%\n                    <jats:italic>vs<\/jats:italic>\n                    . 35.7%) and F1 score (74.1%\n                    <jats:italic>vs<\/jats:italic>\n                    . 52.6%), highlighting its potential to reduce missed cases (false negatives) and support clinical decision-making. This study illustrates the translational potential of artificial intelligence for addressing diagnostically complex and high-risk conditions, offering an effective tool to support radiologists in the assessment of suspected radiolucent foreign body aspiration. Code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/ZheChen1999\/FBA_DL\" ext-link-type=\"uri\">https:\/\/github.com\/ZheChen1999\/FBA_DL<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1038\/s41746-025-02097-w","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T08:41:06Z","timestamp":1762764066000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated detection of radiolucent foreign body aspiration on chest CT using deep learning"],"prefix":"10.1038","volume":"8","author":[{"given":"Xiaofan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zhe","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Xun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Fangfang","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Fang","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Shuang","family":"Geng","sequence":"additional","affiliation":[]},{"given":"Qiong","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Junjie","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mingyuan","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Xiaoqing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Rob M.","family":"Ewing","sequence":"additional","affiliation":[]},{"given":"Zehor","family":"Belkhatir","sequence":"additional","affiliation":[]},{"given":"Guqin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hanxiang","family":"Nie","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Weihua","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yihua","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"2097_CR1","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/S0194-5998(00)70063-5","volume":"122","author":"W-C Hsu","year":"2000","unstructured":"Hsu, W.-C. et al. 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