title: 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 ratings in children and time since surgery, and compared with by-proxy parent and nurse estimates of observed pain in youth.
  
  
  RESULTS
  Model detection of pain versus no-pain demonstrated good-to-excellent accuracy (Area under the receiver operating characteristic curve 0.84-0.94) in both ongoing and transient pain conditions. Model detection of pain severity demonstrated moderate-to-strong correlations (r = 0.65-0.86 within; r = 0.47-0.61 across subjects) for both pain conditions. The model performed equivalently to nurses but not as well as parents in detecting pain versus no-pain conditions, but performed equivalently to parents in estimating pain severity. Nurses were more likely than the model to underestimate youth self-reported pain ratings. Demographic factors did not affect model performance.
  
  
  CONCLUSIONS
  CVML pain assessment models derived from automatic facial expression measurements demonstrated good-to-excellent accuracy in binary pain classifications, strong correlations with patient self-reported pain ratings, and parent-equivalent estimation of children's pain levels over typical pain trajectories in youth after appendectomy.
authors.affiliation: Institute for Neural Computation, and.
authors.forename: Karan
authors.initials: K
authors.lastname: Sikka
authors.affiliation: Institute for Neural Computation, and.
authors.forename: Alex A
authors.initials: AA
authors.lastname: Ahmed
authors.affiliation: Department of Pediatrics, University of California San Diego, San Diego, California;
authors.forename: Damaris
authors.initials: D
authors.lastname: Diaz
authors.affiliation: Department of Health Sciences, Northeastern University, Boston, Massachusetts;
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Goodwin
authors.affiliation: Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada;
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Craig
authors.affiliation: Institute for Neural Computation, and Emotient, Inc., San Diego, California; and.
authors.forename: Marian S
authors.initials: MS
authors.lastname: Bartlett
authors.affiliation: Department of Pediatrics, University of California San Diego, San Diego, California; Rady Children's Hospital, San Diego, California jshuang@ucsd.edu.
authors.forename: Jeannie S
authors.initials: JS
authors.lastname: Huang
mesh_terms: D000293:Adolescent; D001062:Appendectomy / Q000379:methods; D002648:Child; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005149:Facial Expression; D005260:Female; D006801:Humans; D010535:Laparoscopy; D008297:Male; D010147:Pain Measurement / Q000379:methods*; D010149:Postoperative Pain / Q000175:diagnosis*; D012372:ROC Curve; D015203:Reproducibility of Results