Literature DB >> 29989992

A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases.

Ghada Zamzmi, Rangachar Kasturi, Dmitry Goldgof, Ruicong Zhi, Terri Ashmeade, Yu Sun.   

Abstract

Bedside caregivers assess infants' pain at constant intervals by observing specific behavioral and physiological signs of pain. This standard has two main limitations. The first limitation is the intermittent assessment of pain, which might lead to missing pain when the infants are left unattended. Second, it is inconsistent since it depends on the observer's subjective judgment and differs between observers. Intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term consequences. To mitigate these limitations, the current standard can be augmented by an automated system that monitors infants continuously and provides quantitative and consistent assessment of pain. Several automated methods have been introduced to assess infants' pain automatically based on analysis of behavioral or physiological pain indicators. This paper comprehensively reviews the automated approaches (i.e., approaches to feature extraction) for analyzing infants' pain and the current efforts in automatic pain recognition. In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.

Entities:  

Mesh:

Year:  2017        PMID: 29989992     DOI: 10.1109/RBME.2017.2777907

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  11 in total

Review 1.  Future roles of artificial intelligence in early pain management of newborns.

Authors:  Md Sirajus Salekin; Peter R Mouton; Ghada Zamzmi; Raj Patel; Dmitry Goldgof; Marcia Kneusel; Sammie L Elkins; Eileen Murray; Mary E Coughlin; Denise Maguire; Thao Ho; Yu Sun
Journal:  Paediatr Neonatal Pain       Date:  2021-08-05

2.  Automated recognition of pain in cats.

Authors:  Marcelo Feighelstein; Ilan Shimshoni; Lauren R Finka; Stelio P L Luna; Daniel S Mills; Anna Zamansky
Journal:  Sci Rep       Date:  2022-06-10       Impact factor: 4.996

3.  Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment.

Authors:  Md Sirajus Salekin; Ghada Zamzmi; Dmitry Goldgof; Rangachar Kasturi; Thao Ho; Yu Sun
Journal:  Comput Biol Med       Date:  2020-11-28       Impact factor: 4.589

4.  Facial geometric feature extraction based emotional expression classification using machine learning algorithms.

Authors:  Murugappan M; Mutawa A
Journal:  PLoS One       Date:  2021-02-18       Impact factor: 3.240

Review 5.  Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions.

Authors:  Marco Leo; Giuseppe Massimo Bernava; Pierluigi Carcagnì; Cosimo Distante
Journal:  Sensors (Basel)       Date:  2022-01-24       Impact factor: 3.576

6.  Computer mediated automatic detection of pain-related behavior: prospect, progress, perils.

Authors:  Kenneth M Prkachin; Zakia Hammal
Journal:  Front Pain Res (Lausanne)       Date:  2021-12-13

Review 7.  Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities.

Authors:  Karen D Davis; Nima Aghaeepour; Andrew H Ahn; Martin S Angst; David Borsook; Ashley Brenton; Michael E Burczynski; Christopher Crean; Robert Edwards; Brice Gaudilliere; Georgene W Hergenroeder; Michael J Iadarola; Smriti Iyengar; Yunyun Jiang; Jiang-Ti Kong; Sean Mackey; Carl Y Saab; Christine N Sang; Joachim Scholz; Marta Segerdahl; Irene Tracey; Christin Veasley; Jing Wang; Tor D Wager; Ajay D Wasan; Mary Ann Pelleymounter
Journal:  Nat Rev Neurol       Date:  2020-06-15       Impact factor: 42.937

8.  Current state of science in machine learning methods for automatic infant pain evaluation using facial expression information: study protocol of a systematic review and meta-analysis.

Authors:  Dan Cheng; Dianbo Liu; Lisa Liang Philpotts; Dana P Turner; Timothy T Houle; Lucy Chen; Miaomiao Zhang; Jianjun Yang; Wei Zhang; Hao Deng
Journal:  BMJ Open       Date:  2019-12-11       Impact factor: 2.692

9.  Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

Authors:  Muhammad Awais; Xi Long; Bin Yin; Chen Chen; Saeed Akbarzadeh; Saadullah Farooq Abbasi; Muhammad Irfan; Chunmei Lu; Xinhua Wang; Laishuan Wang; Wei Chen
Journal:  BMC Res Notes       Date:  2020-11-04

Review 10.  Physiological Measures of Acute and Chronic Pain within Different Subject Groups: A Systematic Review.

Authors:  H Korving; P S Sterkenburg; E I Barakova; L M G Feijs
Journal:  Pain Res Manag       Date:  2020-09-03       Impact factor: 3.037

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