What Facial Features Does the Pediatrician Look to Decide That a Newborn Is Feeling Pain?

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..

Accurate Neonatal Face Detection for Improved Pain Classification in the Challenging NICU Setting

There is a tendency for object detection systems using off-the-shelf algorithms to fail when deployed in complex scenes. The present work describes a case for detecting facial expression in post-surgical neonates (newborns) as a modality for predicting and classifying severe pain in the Neonatal Intensive Care Unit (NICU). Our initial testing showed that both an off-the-shelf face detector and a machine learning algorithm trained on adult faces failed to detect facial expression of neonates in the NICU. We improved accuracy in this complex scene by training a state-of-the-art "You-Only-Look-Once" (YOLO) face detection model using the USF-MNPAD-I dataset of neonate faces. At run-time our trained YOLO model showed a difference of 8.6% mean Average Precision (mAP) and 21.2% Area under the ROC Curve (AUC) for automatic classification of neonatal pain compared with manual pain scoring by NICU nurses. Given the challenges, time and effort associated with collecting ground truth from the faces of post-surgical neonates, here we share the weights from training our YOLO model with these facial expression data. These weights can facilitate the further development of accurate strategies for detecting facial expression, which can be used to predict the time to pain onset in combination with other sensory modalities (body movements, crying frequency, vital signs). Reliable predictions of time to pain onset in turn create a therapeutic window of time wherein NICU nurses and providers can implement safe and effective strategies to mitigate severe pain in this vulnerable patient population.

Pediatricians' focus of sight at pain assessment during a neonatal heel puncture

OBJECTIVE

To evaluate the focus of pediatricians' gaze during the heel prick of neonates.

METHODS

Prospective study in which pediatricians wearing eye tracker glasses evaluated neonatal pain before/after a heel prtick. Pediatricians scored the pain they perceived in the neonate in a verbal analogue numerical scale (0=no pain; 10=maximum pain). The outcomes measured were number and time of visual fixations in upper face, lower face, and hands, in two 10-second periods, before (pre) and after the puncture (post). These outcomes were compared between the periods, and according to pediatricians' pain perception: absent/mild (score: 0-5) and moderate/intense (score: 6-10).

RESULTS

24 pediatricians (31 years old, 92% female) evaluated 24 neonates. The median score attributed to neonatal pain during the heel prick was 7.0 (Interquartile range: 5-8). Compared to pre-, in the post-periods, more pediatricians fixed their gaze on the lower face (63 vs. 92%; p=0.036) and the number of visual fixations was greater on the lower face (2.0 vs. 5.0; p=0.018). There was no difference in the number and time of visual fixations according to the intensity of pain.

CONCLUSIONS

At bedside, pediatricians change their focus of attention on the neonatal face after a painful procedure, focusing mainly on the lower part of the face.

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.