Evidence map›Paper›PMID 34392886›Full record

ReviewAnesthesiology clinics2021

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

Theodora Wingert, Christine Lee, Maxime Cannesson

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Anesthesiology clinics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07253012 (AI-assisted Analgesia Copilot System for Proper Management of Nociception), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07253012 recruitingnot on this mapstarted 2026, after this paper: background citation

AI-assisted Analgesia Copilot System for Proper Management of Nociception

Typeobservational_patient_registrySponsorPontificia Universidad Catolica de ChileRan2026 to 2027Enrolled150ConditionsNociception, Artificial Intelligence (AI), Target Controlled Infusion (TCI), Remifentanil ConsumptionArmsHemodynamic monitor, BIS, TOF, ANI, anesthesia machine and infusion pumps, SEASCAPE
3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Using machine learning methods to investigate the impact of age on the causes of death in patients with early intrahepatic cholangiocarcinoma who underwent surgery.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Theodora WingertUniversity 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.
Christine LeeEdwards Lifesciences, Irvine, CA, USA; Critical Care R&D, 1 Edwards Way, Irvine, CA 92614, USA.
Maxime CannessonUniversity 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.

Funding

UC BRAID Center for Accelerated Innovation COVID-19 diagnostic project developmentU54HL119893 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PALAZZOLO, MICHAEL J · 2013 to 2020
$18.3M
Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical PatientsR01HL144692 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2019 to 2023
$3.5M
Biomedical Informatics Tools for Applied Perioperative PhysiologyR01EB029751 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2020 to 2023
$2.5M
Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instabilityR01GM117622 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI PINSKY, MICHAEL R · 2016 to 2019
$2.4M
NHLBI NIH HHS R01 HL144692NHLBI NIH HHS U54 HL119893NIBIB NIH HHS R01 EB029751NIGMS NIH HHS R01 GM117622
6 · The paper itself

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.

Indexed as

AnesthesiaAnesthesiologyDeep LearningArtificial IntelligenceHumansMachine LearningArtificial intelligenceClosed loop devicesDeep learningMachine learningNeural networks

Identifiers

PMID34392886
PMCPMC9847584

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.