Literature DB >> 31845543

Artificial intelligence, machine learning and the pediatric airway.

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.
© 2019 John Wiley & Sons Ltd.

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


  3 in total

1.  Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection.

Authors:  Shuqin Ni; Xin Li; Xiuna Yi
Journal:  J Healthc Eng       Date:  2022-03-28       Impact factor: 2.682

Review 2.  Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis.

Authors:  Andrej Thurzo; Wanda Urbanová; Bohuslav Novák; Ladislav Czako; Tomáš Siebert; Peter Stano; Simona Mareková; Georgia Fountoulaki; Helena Kosnáčová; Ivan Varga
Journal:  Healthcare (Basel)       Date:  2022-07-08

3.  Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network.

Authors:  Ho Nguyen Anh Tuan; Nguyen Dao Xuan Hai; Nguyen Truong Thinh
Journal:  Comput Math Methods Med       Date:  2022-01-11       Impact factor: 2.238

  3 in total

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