Deep learning-based automatic detection of pediatric non-high-density tracheobronchial foreign bodies using chest computed tomography
Foreign body aspiration (FBA) of non-high-density objects (NHDFBs) in children is a critical pediatric emergency, posing risks of airway obstruction and requiring prompt diagnosis, which currently relies on clinician experience with low-dose computed tomography (LDCT). This study aimed to develop and validate a deep learning (DL) model for the automated detection of tracheobronchial NHDFBs in pediatric LDCT scans. A retrospective, multicenter cohort of 600 children with suspected FBA was utilized, with bronchoscopic confirmation as the gold standard. A ResUnet-based model was trained and evaluated on internal and external validation sets, with its performance systematically compared against junior and senior radiologists. The DL model achieved foreign body detection rates comparable to senior radiologists across training, internal test and external validation cohorts without significant intergroup differences, whereas both outperformed junior radiologists significantly (all p < 0.05). The DL model exhibited drastically shorter reading time (17.5 ± 2.4 s) than senior radiologists (80.2 ± 13.1 s) and junior radiologists (109.3 ± 16.8 s, all p < 0.05). The model maintained stable high diagnostic performance in all cohorts. Its sensitivity was significantly higher than junior radiologists in both validation sets (all p < 0.05), while sensitivity, specificity, PPV and NPV showed no statistical disparities between the DL model and senior radiologists. The DL model yielded AUC values of 0.93, 0.89 and 0.85 in the three cohorts, which were statistically equivalent to those of senior radiologists (0.94, 0.95, 0.90), and substantially superior to junior radiologists (0.82, 0.83, 0.79). The developed DL model achieves expert-level accuracy with superior efficiency for detecting pediatric NHDFBs on LDCT, demonstrating strong potential as a rapid, objective decision-support tool to enhance diagnostic workflows, particularly in settings with limited specialist availability.