Automated detection of radiolucent foreign body aspiration on chest CT using deep learning.
Authors
Affiliations (15)
Affiliations (15)
- Department of Pulmonary and Critical Care Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, China.
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, UK.
- Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, China.
- School of Medicine, Jianghan University, Wuhan, Hubei 430056, China.
- Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA.
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, UK. [email protected].
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK. [email protected].
- Electronics and Computer Science, Digital Health & Biomedical Engineering Group, University of Southampton, Southampton SO17 1BJ, UK. [email protected].
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China. [email protected].
- Department of Respiratory and Critical Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China. [email protected].
- Department of Pulmonary and Critical Care Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, China. [email protected].
- Department of Pulmonary and Critical Care Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, China. [email protected].
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, UK. [email protected].
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK. [email protected].
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK. [email protected].
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 .