According to a new study by BC Children’s Hospital investigators, epidural analgesia during labour and delivery is not associated with autism spectrum disorder (ASD) in children.
In a recent article published in The Clinical Journal of Pain, Dr. Ran Goldman and colleagues at BC Children’s Hospital, found that facial recognition software could improve how doctors, nurses and other care providers measure pain in young children.
“Being able to clearly and objectively measure children’s pain is incredibly important, especially when kids are too young to tell us what hurts,” says Dr. Goldman, an investigator at BC Children’s. “In the emergency room we strive to know how much pain a child is experiencing, so we can tell what pain relief or medication to give.”
In the study, facial recognition was compared to an established system of measuring pain in young children known as the Face, Legs, Activity, Cry, Consolability (FLACC) scale. This 10-point scale measures five different attributes such as whether the child is crying, grimacing or smiling and relaxed or kicking out with their legs.
“We need new methods of pain measurement since it takes a long time to train the team to use the FLACC scale,” says Dr. Goldman. “Even then, it’s usually confined to research studies as it’s often not practical to use at the bedside.”
Despite being one of the most commonly used tools for assessing pain in young children, the FLACC scale is not ideal. One of the challenges with the FLACC scale is that procedures that aren’t actually that painful can still score quite highly. For example, providing an inhaler to a wheezing child can cause their legs to thrash out earning a higher score on the pain scale. Additionally, the scale requires that clinicians measure things like whether a child’s jaw is “clenched” and “quivering” which can be difficult to see and assess.
For Dr. Goldman’s study, 67 children were assessed using both the FLACC scale and facial recognition while having a blood test in the hospital. The research team used a photo burst feature to rapidly take 10 pictures a second of the children’s face and arm before the needle went in, during and afterwards.
The pictures were then uploaded to an online program developed by Microsoft called the Emotion Application Programming Interface. The program tracks eight emotions - anger, contempt, disgust, fear, happiness, sadness, surprise and a neutral emotion. The change between pictures for each emotional state observed is given a score between zero (no correlation) and one (greatest correlation).
By plotting the changes before and after needle pain, the researchers found that drawing a blood sample caused an increase in the sadness emotion and a decrease in neutral emotion.
“We all know that needles hurt, so finding that children become sadder after a drawing blood won’t surprise anyone,” says Dr. Goldman. “But it does show that the latest digital technology can corroborate pain measured using the FLACC system.”
The next step in this research is to assess how well facial recognition app works in older children who are able to communicate their pain and determine how well this technique works for all children.
“More work is needed to determine how accurate and reliable this method can be,” says Dr. Goldman. “But if using a camera on the phone to assess pain proves to be reliable, it would be revolutionary. It’s fast and anyone can use this method to measure pain in real time. In the future, this system could even be used by parents from home for virtual or remote visits to the doctor or emergency room.”
Dr. Goldman is also a Professor in the Faculty of Medicine at University of British Columbia. The study received support from the Clinical Practice Outcome team and Evidence to Innovation group at the BC Children’s Hospital Research Institute.
For the privacy of the children involved, pictures were analyzed and then deleted and were never saved or shared. All procedures were approved by the University of British Columbia / Children's and Women's Research Ethics Ethics Board.
Sakulchit, T., Kuzeljevic, B., Goldman, R. (2019). Evaluation of Digital Face Recognition Technology for Pain Assessment in Young Children. The Journal of Clinical Pain, 35(1), 18-22.