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<p>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% <i>vs</i>. 35.7%) and F1 score (74.1% <i>vs</i>. 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 <a href="https://github.com/ZheChen1999/FBA_DL">https://github.com/ZheChen1999/FBA_DL</a>.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月10日

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

摘要

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.