Automated detection of radiolucent foreign body aspiration on chest CT using deep learning

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-precision metrics. In a blinded independent evaluation cohort, the model outperformed expert radiologists in both recall (71.4% vs. 35.7%) and F1 score (74.1% vs. 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 https://github.com/ZheChen1999/FBA_DL .

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Publication Details

Journal
npj Digital Medicine
Published
2025-11-10
DOI
https://doi.org/10.1038/s41746-025-02097-w
Primary Topic
Foreign Body Medical Cases
Type
article
Field-Weighted Citation Impact
0.00

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article

Automated detection of radiolucent foreign body aspiration on chest CT using deep learning

Fangfang Jiang, Xiaoqing Wang, Hanxiang Nie, Qiong Qian et al.
npj Digital Medicine
Foreign Body Medical Cases
article

Automated detection of radiolucent foreign body aspiration on chest CT using deep learning

Fangfang Jiang, Xiaoqing Wang, Hanxiang Nie, Qiong Qian, Zehor Belkhatir, Yin Wang, Yang Xun, Dan Zheng, Fang Ni, Rob M. Ewing, Zhiyong Tang, Yanhui Hao, Yankun Jiang, Junjie Xu, Zhe Chen, Mingyuan Zhu, Yihua Wang, Xiaofan Liu, Guqin Zhang, Yi Hu, Shuang Geng, Weihua Wang
article en

Abstract

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-precision metrics. In a blinded independent evaluation cohort, the model outperformed expert radiologists in both recall (71.4% vs. 35.7%) and F1 score (74.1% vs. 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 https://github.com/ZheChen1999/FBA_DL .

npj Digital MedicineVol. 8(1)
University of Iowa (US), University Hospital Southampton NHS Foundation Trust (GB), Jianghan University (CN), Wuhan University (CN), NIHR Southampton Biomedical Research Centre (GB), Zhongnan Hospital of Wuhan University (CN), Renmin Hospital of Wuhan University (CN), Central Hospital of Wuhan (CN), University of Southampton (GB), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, China Scholarship Council, Medical Research Council, National Key Research and Development Program of China
Openalex Percentile: Top 51%
Foreign Body Medical Cases
DOIOpen Access PDFOpenAlex
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