Evaluation of an App Based on Artificial Intelligence for Pain Assessment in Pediatric Department
Condition: C23.888.592.612.081
Study type: Observational

Summary: 
      Accurate assessment of pain in Pediatric Department is challenging. However, recent
      publications highlight that children do not receive optimal pain management, particularly in
      Emergency Departments.

      An Artificial Intelligence-based tool could help physicians to optimize analgesia use.
    

Detailed description: 
      Acute pain is the reason that the majority of patients present to the Emergency Department.
      Multiple studies conducted over many years have demonstrated that pain is poorly managed in
      the Emergency Department. This phenomenon has been referred to in the Medical Literature as
      "Oligoanalgesia.". Morever, untreated pain can have short and long term effects in Pediatric
      population, including sensitisation to pain episodes in later life and can affects the
      Neurodevelopment of the child.

      The generally accepted standard for pain assessment is self-reported; however, in preverbal
      children who cannot communicate their pain, age-appropriate behavioural or observational pain
      assessment tools are recommended. Because children are not always able to voice their
      feelings, they completely depend on their caregiving team for the interpretation and
      management of their pain and discomfort. Thus, accurately validated scales to assess pain
      levels are crucial. In France, the EVENDOL is the most used widely scale. The EVENDOL scale
      (from the French Evaluation Enfant Douleur) is used to evaluate pain in children in any
      situation covering a wider age group than other pain scales (birth up to seven years).
      Despite a large number of scales with a variety of Psychometric properties having been
      published in the last decades, to date, there is no criterion standard when considering the
      assessment of pain. As a result, children continue to be suffered pain without adequate pain
      management. International guidelines incorporate the need for prompt recognition of pain.

      Artificial Intelligence (AI) in Medicine is booming and has already proven its worth in terms
      of prevention, monitoring and diagnosis. AI in this field can be used to support clinician
      decision making, allow curiosity-driven care, remove the need to complete mundane tasks,
      improve communication, and facilitate collaboration. Evaluation of the face is central to all
      observational pain assessment tools, as the face is highly accessible and facial expressions
      are considered the most encodable feature of pain Therefore.the investigators aim to develop,
      validate and asses a system based on Digital Face Recognitation for pain assessment in the
      Children's Department.
    

Intervention name(s): intensity of acute pain evaluation
Intervention type(s): Diagnostic Test
Intervention description(s): the facial expression of each patient will be collected from a short video of 10 seconds, follow by with the evaluation of the pain intensity.
Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        2000 patients under 18 year old admitted in Emergency Department