Automated Detection of Pediatric Foreign Body Aspiration from Chest X-rays Using Machine Learning

Abstract

Objective/Hypothesis: Standard chest radiographs are a poor diagnostic tool for pediatric foreign body aspiration. Machine learning may improve upon the diagnostic capabilities of chest radiographs. The objective is to develop a machine learning algorithm that improves the diagnostic capabilities of chest radiographs in pediatric foreign body aspiration. Method: This retrospective, diagnostic study included a retrospective chart review of patients with a potential diagnosis of FBA from 2010 to 2020. Frontal view chest radiographs were extracted, processed, and uploaded to Google AutoML Vision. The developed algorithm was then evaluated against a pediatric radiologist. Results: The study selected 566 patients who were presented with a suspected diagnosis of foreign body aspiration. One thousand six hundred and eighty eight chest radiograph images were collected. The sensitivity and specificity of the radiologist interpretation were 50.6% (43.1–58.0) and 88.7% (85.3–91.5), respectively. The sensitivity and specificity of the algorithm were 66.7% (43.0–85.4) and 95.3% (90.6–98.1), respectively. The precision and recall of the algorithm were both 91.8% with an AuPRC of 98.3%. Conclusion: Chest radiograph analysis augmented with machine learning can diagnose foreign body aspiration in pediatric patients at a level similar to a read performed by a pediatric radiologist despite only using single-view, fixed images. Overall, this study highlights the potential and capabilities of machine learning in diagnosing conditions with a wide range of clinical presentations. Level of Evidence: 3 Laryngoscope, 134:3807–3814, 2024.