Thomas Küstner

Adult endotracheal tube AI models have been hard to apply to newborns, where annotated imaging is scarce and privacy rules block data sharing. An international retrospective study across 30 neonatal intensive care units in 20 countries and territories used continual learning: the model passes from hospital to hospital, and each site retrains it on 50 local radiographs inside its own environment without sharing patient data. The final model averaged 0.67 cm combined endotracheal tube tip and carina localization error, versus 1.29 cm for hospital-specific fine-tuning and 4.30 cm for the adult model used directly, improving localization at 29 of 30 sites and deep-intubation detection at 27 of 30.

Adult endotracheal tube AI models have been hard to apply to newborns, where annotated imaging is scarce and privacy rules block data sharing. An international retrospective study across 30 neonatal intensive care units in 20 countries and territories used continual learning: the model passes from hospital to hospital, and each site retrains it on 50 local radiographs inside its own environment without sharing patient data. The final model averaged 0.67 cm combined endotracheal tube tip and carina localization error, versus 1.29 cm for hospital-specific fine-tuning and 4.30 cm for the adult model used directly, improving localization at 29 of 30 sites and deep-intubation detection at 27 of 30.