, an artificial intelligence system developed by Lunit identified improperly positioned endotracheal tube on chest radiographs obtained after ETT insertion, as well as on chest radiographs obtained from patients in the ICU at two institutions.
"Automated AI identification of improper ETT position on chest radiograph may allow earlier repositioning and thereby reduce complications," wrote corresponding author Eui Jin Hwang, MD, Ph.D., from the department of radiology at Korea's Seoul National University Hospital. Hwang et al.
Ultimately, in three patient samples from two different institutions, an AI system identified ETT presence with of 99.2–100%; and specificity of 94.5–98.7%, improper ETT position with sensitivity of 72.5–83.7% and specificity of 92.0–100%, and critical ETT position with sensitivity of 100% in all samples and specificity of 96.7–100%.Ji Yeong An et al, Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions,