Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.
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Abstract
Background: Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. Objective: To evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) system for detecting ETT presence and position on chest radiographs in three patient samples from two different institutions. Methods: This retrospective study included 539 chest radiographs obtained immediately after ETT insertion from January 1, 2020 to March 31, 2020 in 505 patients (293 men, 212 women; mean age, 63 years) from institution A (sample A); 637 chest radiographs obtained from January 1, 2020 to January 3, 2020 in 304 patients (158 men, 147 women; mean age, 63 years) in the ICU (with or without an ETT) from institution A (sample B); and 546 chest radiographs obtained from January 1, 2020 to January 20, 2020 in 83 patients (54 men, 29 women; mean age, 70 years) in the ICU (with or without an ETT) from institution B (sample C). A commercial DL-based AI system was used to identify ETT presence and measure ETT tip-to-carina distance (TCD). Reference standard for proper ETT position was TCD between 3 cm and 7 cm, determined by human readers. Critical ETT position was separately defined as ETT tip below the carina or TCD ≤1 cm. ROC analysis was performed. Results: AI had sensitivity and specificity for identification of ETT presence of 100.0% and 98.7% (sample B) and 99.2% and 94.5% (sample C). AI had sensitivity and specificity for identification of improper ETT position of 72.5% and 92.0% (sample A), 78.9% and 100.0% (sample B), and 83.7% and 99.1% (sample C). At threshold y-axis TCD ≤2 cm, AI had sensitivity and specificity for critical ETT position of 100.0% and 96.7% (sample A), 100.0% and 100.0% (sample B), and 100.0% and 99.2% (sample C). Conclusion: AI identified improperly positioned ETTs on chest radiographs obtained after ETT insertion, as well as on chest radiographs obtained from patients in the ICU at two institutions. Clinical Impact: Automated AI identification of improper ETT position on chest radiograph may allow earlier repositioning and thereby reduce complications.