Evidence map›Paper›PMID 29997313›Full record

ArticleJournal of clinical medicine2018

Automatic Infants' Pain Assessment by Dynamic Facial Representation: Effects of Profile View, Gestational Age, Gender, and Race.

Ruicong Zhi, Ghada Zamzmi Dmitry Zamzmi, Dmitry Goldgof, Terri Ashmeade, Yu Sun

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 6% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

  1. Artificial intelligence in pediatric pain: a systematic review.BMC medical informatics and decision making · 2026
    Pooled it
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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

5 authors at 2 institutions in 2 countries.

Ruicong ZhiSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China. zhirc_research@126.com.
Ghada Zamzmi Dmitry ZamzmiDepartment of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. ghadh@mail.usf.edu.
Dmitry GoldgofDepartment of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. goldgof@mail.usf.edu.
Terri AshmeadeCollege of Medicine Pediatrics, University of South Florida, Tampa, FL 33620, USA. tashmead@health.usf.edu.
Yu SunDepartment of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. yusun@mail.usf.edu.
University of South Florida · USUniversity of Science and Technology Beijing · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infants' early exposure to painful procedures can have negative short and long-term effects on cognitive, neurological, and brain development. However, infants cannot express their subjective pain experience, as they do not communicate in any language. Facial expression is the most specific pain indicator, which has been effectively employed for automatic pain recognition. In this paper, dynamic pain facial expression representation and fusion scheme for automatic pain assessment in infants is proposed by combining temporal appearance facial features and temporal geometric facial features. We investigate the effects of various factors that influence pain reactivity in infants, such as individual variables of gestational age, gender, and race. Different automatic infant pain assessment models are constructed, depending on influence factors as well as facial profile view, which affect the model ability of pain recognition. It can be concluded that the profile-based infant pain assessment is feasible, as its performance is almost as good as that of the whole face. Moreover, gestational age is the most influencing factor for pain assessment, and it is necessary to construct specific models depending on it. This is mainly because of a lack of behavioral communication ability in infants with low gestational age, due to limited neurological development. To our best knowledge, this is the first study investigating infants' pain recognition, highlighting profile facial views and various individual variables.

Indexed as

dynamic facial representationgendergestational ageinfants’ pain assessmentprofile viewrace

Identifiers

PMID29997313
PMCPMC6069472
OpenAlexW2867606265

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.