Evidence map›Paper›PMID 35847210›Full record

ArticleFrontiers in neurology2022

Human vs. Machine Learning Based Detection of Facial Weakness Using Video Analysis.

Chad M Aldridge, Mark M McDonald, Mattia Wruble, Yan Zhuang, Omar Uribe, Timothy L McMurry, Iris Lin, Haydon Pitchford, Brett J Schneider, William A Dalrymple and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 16% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
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

15 authors at 3 institutions in 1 country.

Chad M AldridgeDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Mark M McDonaldDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Mattia WrubleDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Yan ZhuangDepartment of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States.
Omar UribeDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Timothy L McMurryDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA, United States.
Iris LinDepartment of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Haydon PitchfordDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Brett J SchneiderDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
William A DalrympleDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Joseph F CarreraDepartment of Neurology, University of Michigan, Ann Arbor, MI, United States.
Sherita ChapmanDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Bradford B WorrallDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
Gustavo K RohdeDepartment of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States.
Andrew M SoutherlandDepartment of Neurology, University of Virginia, Charlottesville, VA, United States.
University of Virginia · USUniversity of Michigan–Ann Arbor · USUniversity of Pittsburgh · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Current EMS stroke screening tools facilitate early detection and triage, but the tools' accuracy and reliability are limited and highly variable. An automated stroke screening tool could improve stroke outcomes by facilitating more accurate prehospital diagnosis and delivery. We hypothesize that a machine learning algorithm using video analysis can detect common signs of stroke. As a proof-of-concept study, we trained a computer algorithm to detect presence and laterality of facial weakness in publically available videos with comparable accuracy, sensitivity, and specificity to paramedics. Methods and Results: We curated videos of people with unilateral facial weakness ( Conclusions: These preliminary results suggest that a machine learning algorithm using computer vision analysis can detect unilateral facial weakness in pre-recorded videos with an accuracy and sensitivity comparable to trained paramedics. Further research is warranted to pursue the concept of augmented facial weakness detection and external validation of this algorithm in independent data sets and prospective patient encounters.

Indexed as

access to carecerebrovascular diseasecomputer visiondiagnostic testinfarctionmachine learningstroke

Identifiers

PMID35847210
PMCPMC9284117
OpenAlexW4283749599

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.