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

作者：Xiaofan Liu, Zhe Chen, Zhiyong Tang, Yang Xun, Yankun Jiang, Dan Zheng, Fangfang Jiang, Fang Ni, Shuang Geng, Qiong Qian, Yanhui Hao, Junjie Xu, Yin Wang, Yin Wang, Mingyuan Zhu, Xiaoqing Wang, Rob M. Ewing, Zehor Belkhatir, Guqin Zhang, Hanxiang Nie, Yi Hu, Yihua Wang, Yihua Wang, Yihua Wang · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-02097-w · 被引用次数：5 · 研究领域：Foreign Body Medical Cases、Autopsy Techniques and Outcomes、Lung Cancer Diagnosis and Treatment

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 .