Looking through Providers' Eyes: Pain in the Neonatal Intensive Care Unit.

OBJECTIVE: Evaluate the pain of critically ill newborns is a challenge because of the devices for cardiorespiratory support. This study aim to verify the adults' gaze when assessing the critically ill neonates' pain at bedside. STUDY DESIGN: Cross-sectional study in which pediatricians, nursing technicians, and parents evaluated critically ill neonates' pain at bedside, for 20 seconds with eye-tracking glasses. At the end, they answered whether the neonate was in pain or not. Visual tracking outcomes: number and time of visual fixations in four areas of interest (AOI) (face, trunk, and upper [UL] and lower [LL] limbs) were compared between groups and according to pain perception (present/absent). RESULTS: A total of 62 adults (21 pediatricians, 23 nursing technicians, 18 parents) evaluated 27 neonates (gestational age: 31.8 ± 4.4 weeks; birth weight: 1,645 ± 1,234 g). More adults fixed their gaze on the face (96.8%) and trunk (96.8%), followed by UL (74.2%) and LL (66.1%). Parents performed a greater number of fixations on the trunk than nursing technicians (11.0 vs. 5.5 vs. 6.0; p = 0.023). Controlled for visual tracking variables, each second of eye fixation in AOI (1.21; 95% confidence interval [CI]: 1.03-1.42; p = 0.018) and UL (1.07; 95% CI: 1.03-1.10; p < 0.001) increased the chance of perceiving the presence of pain. CONCLUSION: Adults, when assessing at bedside critically ill newborns' pain, fixed their eyes mainly on the face and trunk. The time spent looking at the UL was associated with the perception of pain presence. KEY POINTS: · Pain assessment in critically ill newborns is a challenge.. · To assess critically ill neonates' pain, adults mainly look at the face and trunk.. · Looking at the upper limbs also helps in assessing critically ill neonates' pain..

Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units.

OBJECTIVE: To describe the challenges and perspectives of the automation of pain assessment in the Neonatal Intensive Care Unit. DATA SOURCES: A search for scientific articles published in the last 10 years on automated neonatal pain assessment was conducted in the main Databases of the Health Area and Engineering Journal Portals, using the descriptors: Pain Measurement, Newborn, Artificial Intelligence, Computer Systems, Software, Automated Facial Recognition. SUMMARY OF FINDINGS: Fifteen articles were selected and allowed a broad reflection on first, the literature search did not return the various automatic methods that exist to date, and those that exist are not effective enough to replace the human eye; second, computational methods are not yet able to automatically detect pain on partially covered faces and need to be tested during the natural movement of the neonate and with different light intensities; third, for research to advance in this area, databases are needed with more neonatal facial images available for the study of computational methods. CONCLUSION: There is still a gap between computational methods developed for automated neonatal pain assessment and a practical application that can be used at the bedside in real-time, that is sensitive, specific, and with good accuracy. The studies reviewed described limitations that could be minimized with the development of a tool that identifies pain by analyzing only free facial regions, and the creation and feasibility of a synthetic database of neonatal facial images that is freely available to researchers.

Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units

Abstract Objective: To describe the challenges and perspectives of the automation of pain assessment in the Neonatal Intensive Care Unit. Data sources: A search for scientific articles published in the last 10 years on automated neonatal pain assessment was conducted in the main Databases of the Health Area and Engineering Journal Portals, using the descriptors: Pain Measurement, Newborn, Artificial Intelligence, Computer Systems, Software, Automated Facial Recognition. Summary of findings: Fifteen articles were selected and allowed a broad reflection on first, the literature search did not return the various automatic methods that exist to date, and those that exist are not effective enough to replace the human eye; second, computational methods are not yet able to automatically detect pain on partially covered faces and need to be tested during the natural movement of the neonate and with different light intensities; third, for research to advance in this area, databases are needed with more neonatal facial images available for the study of computational methods. Conclusion: There is still a gap between computational methods developed for automated neonatal pain assessment and a practical application that can be used at the bedside in real-time, that is sensitive, specific, and with good accuracy. The studies reviewed described limitations that could be minimized with the development of a tool that identifies pain by analyzing only free facial regions, and the creation and feasibility of a synthetic database of neonatal facial images that is freely available to researchers.