Artificial intelligence, machine learning and the pediatric airway. Issue 3 (2nd January 2020)

• Record Type: Journal Article
• Authors: Matava, Clyde; Pankiv, Evelina; Ahumada, Luis; Weingarten, Benjamin; Simpao, Allan
• Editors: von Ungern‐Sternberg, Britta
• Is Part Of: Paediatric anaesthesia. Volume 30:Issue 3(2020:Mar.)
• Page Start: 264
• Page End: 268
• Publication Date: 2020-01-02
• Subjects: age -- adolescent -- age -- airway difficult -- age -- infant -- age -- neonate -- airway -- child; Pediatric anesthesia -- Periodicals; 617.96798
• DOI: 10.1111/pan.13792
• Languages: English
• ISSNs: 1155-5645

Abstract

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.