DNIC Using Deep Learning & Artificial Intelligence

Status Suspended

Clinical Trial Summary

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.

Clinical Trial Description

This study aims: 1. To develop and validate a predictive tool (using deep learning and artificial intelligence) to estimate the efficacy of pain control mechanisms. 2. 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. ;

Study Design

Related Conditions & MeSH terms

Chronic Pain

Clinical Trial Details

NCT number	NCT04896827
Study type	Observational
Source	Université de Sherbrooke
Contact
Status	Suspended
Phase
Start date	April 5, 2022
Completion date	December 2024

View Details

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Clinical trials are conducted in a series of steps, called phases - each phase is designed to answer a separate research question.

Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.

DNIC Using Deep Learning and Artificial Intelligence

Clinical Trial Summary

Clinical Trial Description

Study Design

Related Conditions & MeSH terms