Automated Assessment of Children's Postoperative Pain Using Computer Vision.

ABSTRACT:

Background

Current pain assessment methods in youth are suboptimal and vulnerable to bias and underrecognition of clinical pain. Facial expressions are a sensitive, specific biomarker of the presence and severity of pain, and computer vision (CV) and machine-learning (ML) techniques enable reliable, valid measurement of pain-related facial expressions from video. We developed and evaluated a CVML approach to measure pain-related facial expressions for automated pain assessment in youth.

Methods

A CVML-based model for assessment of pediatric postoperative pain was developed from videos of 50 neurotypical youth 5 to 18 years old in both endogenous/ongoing and exogenous/transient pain conditions after laparoscopic appendectomy. Model accuracy was assessed for self-reported pain

SUBMITTER: Sikka K 

PROVIDER: S-EPMC4485009 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature