DNIC Using Deep Learning and Artificial Intelligence

NCT ID: NCT04896827

Last Updated: 2024-12-06

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

SUSPENDED

Total Enrollment

244 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-04-05

Study Completion Date

2025-12-31

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Chronic Pain

Keywords

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Conditioned pain modulation (CPM) Diffuse Noxious Inhibitory Controls (DNIC) Pressure pain threshold (PPT) Reference values Clinical decision rule Deep learning Artificial intelligence

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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Chronic pain

Patients with chronic pain (n=100)

Conditioned pain modulation test

Intervention Type OTHER

Conditioned pain modulation (CPM) protocol consist of evaluating pain during a heat test, before and after one conditioning stimulus (cold water bath); 3 cameras will be capturing participants' facial expressions during the tests.

Healthy participants

Healthy participants (n=144)

Conditioned pain modulation test

Intervention Type OTHER

Conditioned pain modulation (CPM) protocol consist of evaluating pain during a heat test, before and after one conditioning stimulus (cold water bath); 3 cameras will be capturing participants' facial expressions during the tests.

Interventions

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Conditioned pain modulation test

Conditioned pain modulation (CPM) protocol consist of evaluating pain during a heat test, before and after one conditioning stimulus (cold water bath); 3 cameras will be capturing participants' facial expressions during the tests.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* 18-79 years old
* No chronic pain
* Able to provide consent


* 18-79 years old
* Chronic pain (chronic pain is defined by any regular pain for more than 6 months)
* Able to provide consent

Exclusion Criteria

* Cardiovascular disease (arrhythmia, cerebrovascular accident, infarction...)
* Raynaud syndrome
* Severe psychiatric disease (dementia, schizophrenia, psychosis, major depression)
* Injuries or loss sensitivity to their forearms or hands
* Pregnant women or in post-partum period (\<1 year)

Participants with chronic pain


* Cardiovascular disease (arrhythmia, cerebrovascular accident, infarction...)
* Raynaud syndrome
* Severe psychiatric disease (dementia, schizophrenia, psychosis, major depression)
* Injuries or loss sensitivity to their forearms or hands
* Pregnant women or in post-partum period (\<1 year)
* Chronic pain caused by cancer or migraine
Minimum Eligible Age

18 Years

Maximum Eligible Age

79 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Lucine

INDUSTRY

Sponsor Role collaborator

Centre for Research of CHUS (CRCHUS)

UNKNOWN

Sponsor Role collaborator

Université de Sherbrooke

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Guillaume Léonard, PhD

Role: PRINCIPAL_INVESTIGATOR

Université de Sherbrooke

Locations

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Université de Sherbrooke

Sherbrooke, Quebec, Canada

Site Status

Countries

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Canada

Other Identifiers

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2021-4227

Identifier Type: -

Identifier Source: org_study_id