In the past, bronchial endoscopy called for the use of the rigid bronchoscope and was consequently used very infrequently in children. The advent of thin and ultrathin fiberoptic bronchoscopes has considerably extended the use of this procedure, even in the neonatal field.
In flexible endoscopy techniques, such as bronchoscopy, there is often a challenge visualizing the path from start to target based on preoperative data and accessing these during the procedure.
Aspirations of foreign bodies are life threatening among children and elderly patients requiring urgent medical assistance.
Simulation-based bronchoscopy training is increasingly used, but effectiveness
Plastic bronchitis (PB) is an extremely rare clinical entity where inspissated cast is found in endobronchial airway leading to respiratory distress. It is a large gelatinous or rigid branching airway cast.
The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes.
Artificial intelligence and machine learning are rapidly expanding fields with increasing relevance in anesthesia and, in particular, airway management.
Pediatric-specific difficult airway guidelines include algorithms for 3 scenarios: unanticipated difficult tracheal intubation, difficult mask ventilation, and cannot intubate/cannot ventilate. While rare, these instances may require front-of-neck access (FONA) to secure an airway until a definitive airway can be established.
As it has matured over the years, bronchoscopy is now frequently utilized to diagnose and treat a vast range of pulmonary disorders.
Foreign body aspiration is a leading cause of death in children 1–3 years old, although mortality is low for children who reach the hospital.