Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery

Theodora Wingert; Christine Lee; Maxime Cannesson

doi:10.1016/j.anclin.2021.03.012

Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery

Anesthesiol Clin. 2021 Sep;39(3):565-581. doi: 10.1016/j.anclin.2021.03.012. Epub 2021 Jul 12.

Authors

Theodora Wingert¹, Christine Lee², Maxime Cannesson³

Affiliations

¹ University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA; Department of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Suite 3325, Los Angeles, CA 90095-7403, USA. Electronic address: twingert@mednet.ucla.edu.
² Edwards Lifesciences, Irvine, CA, USA; Critical Care R&D, 1 Edwards Way, Irvine, CA 92614, USA.
³ University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA; Department of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Suite 3325, Los Angeles, CA 90095-7403, USA.

Abstract

With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.

Keywords: Artificial intelligence; Closed loop devices; Deep learning; Machine learning; Neural networks.

Publication types

Review

MeSH terms

Anesthesia*
Anesthesiology*
Artificial Intelligence
Deep Learning*
Humans
Machine Learning

Abstract

Publication types

MeSH terms

Grants and funding