| Literature DB >> 31845543 |
Clyde Matava1,2, Evelina Pankiv1,2, Luis Ahumada3, Benjamin Weingarten1, Allan Simpao4.
Abstract
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.Keywords: adolescent; age; airway; airway difficult; child; infant; neonate
Mesh:
Year: 2020 PMID: 31845543 DOI: 10.1111/pan.13792
Source DB: PubMed Journal: Paediatr Anaesth ISSN: 1155-5645 Impact factor: 2.556