Evaluation of an App Based on Artificial Intelligence for Pain Assessment in Pediatric Department

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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.

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.

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