Artificial intelligence, machine learning and the pediatric airway.

Artificial intelligence and machine learning are rapidly expanding fields with increasing relevance in anesthesia and, in particular, airway management. The ability of artificial intelligence and machine learning algorithms to recognize patterns from large volumes of complex data makes them attractive for use in pediatric anesthesia airway management. The purpose of this review is to introduce artificial intelligence, machine learning, and deep learning to the pediatric anesthesiologist. Current evidence and developments in artificial intelligence, machine learning, and deep learning relevant to pediatric airway management are presented. We critically assess the current evidence on the use of artificial intelligence and machine learning in the assessment, diagnosis, monitoring, procedure assistance, and predicting outcomes during pediatric airway management. Further, we discuss the limitations of these technologies and offer areas for focused research that may bring pediatric airway management anesthesiology into the era of artificial intelligence and machine learning.

Pulmonary and laryngeal tuberculosis in a 25-weeks' gestation parturient, diagnosed after failed tracheal intubation.

A pregnant woman at 25weeks of gestation was diagnosed with laryngeal tuberculosis following a failed intubation for upper gastrointestinal endoscopy. Laryngeal tuberculosis represents approximately 1% of all cases of tuberculosis in the United States and presents a unique diagnostic challenge, because accompanying laryngeal changes are both varied and nonspecific. This report highlights both the challenges of the pregnant airway and the diagnosis and treatment of laryngeal tuberculosis.

The 'Can't intubate can't oxygenate' scenario in pediatric anesthesia: a comparison of the Melker cricothyroidotomy kit with a scalpel bougie technique.

BACKGROUND: While the majority of pediatric intubations are uncomplicated, the 'Can't intubate, Can't Oxygenate' scenario (CICO) does occur. With limited management guidelines available, CICO is still a challenge even to experienced pediatric anesthetists. OBJECTIVES: To compare the COOK Melker cricothyroidotomy kit (CM) with a scalpel bougie (SB) technique for success rate and complication rate in a tracheotomy on a cadaveric 'infant airway' animal model. METHODS: Two experienced proceduralists repeatedly attempted tracheotomy in eight rabbits, alternately using CM and SB (4 fr) technique. The first attempt was performed at the level of the first tracheal cartilage with subsequent experimental trials of insertion progressively more caudad. Success was defined as intratracheal placement of cannula as seen on bronchoscope. Complications were assessed both by bronchoscopic and macropathological appearance. RESULTS: 32 attempts were made at tracheotomy. CM had an overall success rate of 100% compared to a 75% success rate for SB. Success rate for the first attempt was dependent on the level of the tracheotomy (Level 1 100%, level 2 62.5% and level 3 & 4 25%). While CM was associated with lateral and/or posterior wall damage on bronchoscopy/macropathology in 6% of 19% and 25% of 50% respectively, the damage observed was greater and more frequent with SB (19%/44% and 31%/50%, respectively). CONCLUSIONS: At level 1, the first attempt success rate was 100% for both devices. Overall CM showed a better success rate than SB; however, both techniques were associated with significant complication rates, which were more pronounced following the scalpel bougie technique.