Pain assessment is a common challenge among clinicians, often even leading to discord with patients begging for relief. A team at the University of California, San Diego School of Medicine developed a computer vision algorithm that can estimate pain levels from videos of patients faces at least as well as nurses can.
The team tested the software on kids five to 18 years of age that went through a laparoscopic appendectomy, with a camera filming their faces in the post-op. They compared the estimates the software produced to those of the kids’ parents and the nurses taking care of them, showing that it resulted in about the same ability of detecting pain as the nurses, and equivalently well in detecting the severity of pain as the parents. This was all also compared to the kids’ own reporting of pain levels, with the study also showing that nurses tended to understate the pain of the patients compared to the computer software.
From the study in journal Pediatrics:
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-
Study in journal Pediatrics: Automated Assessment of Children’s Postoperative Pain Using Computer Vision…