This study aims:~To develop and validate a predictive tool (using deep learning and artificial intelligence) to estimate the efficacy of pain control mechanisms.~To estimate references values for facial expressions of pain control mechanisms in healthy and in chronic pain participants.~The target population will be healthy volunteers and volunteers with chronic pain, male and female, stratified by age.~The reference values (healthy volunteers) will be established via a non-parametric method for a standard conditioned pain modulation (CPM) protocol in which two stimuli tests of the same intensity and nature (heat) will be applied before and after the application of another conditioning stimulus (cold water bath). The perceived pain difference between the 1st and 2nd stimuli tests will reflect the intensity of the DNICs. Participants' facial expressions will be captured simultaneously by three cameras during the CPM testing.~These results will be compared to those from volunteers suffering with chronic pain. The clinical decision rule will result from clinical and paraclinical elements correlating with the amplitude of the efficacy of CPM (serum noradrenaline, intensity of pain, heart rate and blood pressure measurements, psychometric questionnaires assessing anxiety, depressive feelings and pain catastrophizing). Logistic regression analysis will determine the best predictors of a CPM deficit.
Chronic pain (CP) is disabling for people triggering important costs for society. A deficit of diffuse noxious inhibitory controls (DNIC) is one of the CP mechanisms. DNICs are evaluated in research setting using a CPM protocol (conditioned pain modulation). There is a lack of reference values on the effectiveness of DNICs. Wider research on DNIC will help to understand CP and to develop a clinical screening test evaluating DNICs. This study aims more specifically to determine whether it is possible to develop a facial recognition system to automate pain measurement and the effectiveness of pain control mechanisms.