Ignite Creation Date:
2026-03-26 @ 3:21 PM
Ignite Modification Date:
2026-04-01 @ 10:28 PM
Study NCT ID:
NCT04896827
Status:
SUSPENDED
Last Update Posted:
2024-12-06
First Post:
2021-05-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
DNIC Using Deep Learning and Artificial Intelligence
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D059350', 'term': 'Chronic Pain'}], 'ancestors': [{'id': 'D010146', 'term': 'Pain'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 244}, 'patientRegistry': False}, 'statusModule': {'whyStopped': 'The funding of the study is challenged. We might resume enrollment if new funds are found or will terminate the study.', 'overallStatus': 'SUSPENDED', 'startDateStruct': {'date': '2022-04-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-12', 'completionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-12-03', 'studyFirstSubmitDate': '2021-05-05', 'studyFirstSubmitQcDate': '2021-05-17', 'lastUpdatePostDateStruct': {'date': '2024-12-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-05-21', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Conditioned pain modulation (CPM) profiles', 'timeFrame': 'Once, at baseline, at recruitment (comparison between 1st and 2nd test, after the conditioning stimuli)', 'description': 'Sensitivity and specificity of the classification algorithm according to different profiles (normal vs altered) of conditioned pain modulation, as defined by the change of pain perception before and after the cold water bath measured by computerized visual analog scale (CoVAS) ranging from 0 \\[no pain\\] to 100 \\[most intense pain that could be tolerated\\] in healthy and in chronic pain volunteers together.'}, {'measure': 'Temporal summation profiles', 'timeFrame': 'Once, at baseline, at recruitment (during the first stimuli test)', 'description': 'Sensitivity and specificity of the classification algorithm according to different profiles (normal vs altered) of temporal summation, as defined by the change of pain perception during the first stimuli test measured by computerized visual analog scale (CoVAS) ranging from 0 \\[no pain\\] to 100 \\[most intense pain that could be tolerated\\] in healthy and in chronic pain volunteers together.'}], 'secondaryOutcomes': [{'measure': 'Conditioned pain modulation (CPM) profiles of healthy volunteers', 'timeFrame': 'Once, at baseline, at recruitment (comparison between 1st and 2nd test, after the conditioning stimuli', 'description': 'Conditioned pain modulation, as defined by the change of pain perception before and after the cold water bath measured by computerized visual analog scale (CoVAS) ranging from 0 \\[no pain\\] to 100 \\[most intense pain that could be tolerated\\] only in healthy volunteers.'}, {'measure': 'Temporal summation profiles of healthy volunteers', 'timeFrame': 'Once, at baseline, at recruitment (during the first stimuli test)', 'description': 'Temporal summation, as defined by the change of pain perception during the first stimuli test measured by computerized visual analog scale (CoVAS) ranging from 0 \\[no pain\\] to 100 \\[most intense pain that could be tolerated\\] only in healthy volunteers.'}, {'measure': 'Conditioned pain modulation (CPM) profiles of volunteers with chronic pain', 'timeFrame': 'Once, at baseline, at recruitment (comparison between 1st and 2nd test, after the conditioning stimuli)', 'description': 'Conditioned pain modulation, as defined by the change of pain perception before and after the cold water bath measured by computerized visual analog scale (CoVAS) ranging from 0 \\[no pain\\] to 100 \\[most intense pain that could be tolerated\\] only in volunteers with chronic pain.'}, {'measure': 'Temporal summation profiles of volunteers with chronic pain', 'timeFrame': 'Once, at baseline, at recruitment (during the first stimuli test)', 'description': 'Temporal summation, as defined by the change of pain perception during the first stimuli test measured by computerized visual analog scale (CoVAS) ranging from 0 \\[no pain\\] to 100 \\[most intense pain that could be tolerated\\] only in volunteers with chronic pain.'}, {'measure': 'Demographic factors', 'timeFrame': 'Once, at baseline, at recruitment', 'description': 'Association of demographic factors (age, gender) with different response profiles of CPM and temporal summation established by the algorithm.'}, {'measure': 'Psychologic factors', 'timeFrame': 'Once, at baseline, at recruitment', 'description': 'Association of psychologic factors (anxiety measured with HADS questionnaire) with different response profiles of CPM and temporal summation established by the algorithm.'}, {'measure': 'Physiologic factors', 'timeFrame': 'Once, at baseline, at recruitment', 'description': 'Association of physiologic factors (continuous blood pressure, heart rate, electrodermal activity) with different response profiles of CPM and temporal summation established by the algorithm.'}, {'measure': 'Facial expressions and postures', 'timeFrame': 'Once, at baseline, at recruitment', 'description': 'Association of facial expressions and postures with different response profiles of CPM and temporal summation established by the algorithm.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Conditioned pain modulation (CPM)', 'Diffuse Noxious Inhibitory Controls (DNIC)', 'Pressure pain threshold (PPT)', 'Reference values', 'Clinical decision rule', 'Deep learning', 'Artificial intelligence'], 'conditions': ['Chronic Pain']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': 'This study aims:\n\n1. To develop and validate a predictive tool (using deep learning and artificial intelligence) to estimate the efficacy of pain control mechanisms.\n2. To estimate references values for facial expressions of pain control mechanisms in healthy and in chronic pain participants.\n\nThe target population will be healthy volunteers and volunteers with chronic pain, male and female, stratified by age.\n\nThe 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.\n\nThese 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '79 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Male and female volunteers, with and without chronic pain.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Healthy participants\n\nInclusion Criteria:\n\n* 18-79 years old\n* No chronic pain\n* Able to provide consent\n\nExclusion Criteria:\n\n* Cardiovascular disease (arrhythmia, cerebrovascular accident, infarction...)\n* Raynaud syndrome\n* Severe psychiatric disease (dementia, schizophrenia, psychosis, major depression)\n* Injuries or loss sensitivity to their forearms or hands\n* Pregnant women or in post-partum period (\\<1 year)\n\nParticipants with chronic pain\n\nInclusion Criteria:\n\n* 18-79 years old\n* Chronic pain (chronic pain is defined by any regular pain for more than 6 months)\n* Able to provide consent\n\nExclusion Criteria:\n\n* Cardiovascular disease (arrhythmia, cerebrovascular accident, infarction...)\n* Raynaud syndrome\n* Severe psychiatric disease (dementia, schizophrenia, psychosis, major depression)\n* Injuries or loss sensitivity to their forearms or hands\n* Pregnant women or in post-partum period (\\<1 year)\n* Chronic pain caused by cancer or migraine'}, 'identificationModule': {'nctId': 'NCT04896827', 'briefTitle': 'DNIC Using Deep Learning and Artificial Intelligence', 'organization': {'class': 'OTHER', 'fullName': 'Université de Sherbrooke'}, 'officialTitle': 'Reference Values and Clinical Screening Test of Diffuse Noxious Inhibitory Controls (DNIC) Using Deep Learning and Artificial Intelligence', 'orgStudyIdInfo': {'id': '2021-4227'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Chronic pain', 'description': 'Patients with chronic pain (n=100)', 'interventionNames': ['Other: Conditioned pain modulation test']}, {'label': 'Healthy participants', 'description': 'Healthy participants (n=144)', 'interventionNames': ['Other: Conditioned pain modulation test']}], 'interventions': [{'name': 'Conditioned pain modulation test', 'type': 'OTHER', 'description': "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.", 'armGroupLabels': ['Chronic pain', 'Healthy participants']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'J1H 5N4', 'city': 'Sherbrooke', 'state': 'Quebec', 'country': 'Canada', 'facility': 'Université de Sherbrooke', 'geoPoint': {'lat': 45.40008, 'lon': -71.89908}}], 'overallOfficials': [{'name': 'Guillaume Léonard, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Université de Sherbrooke'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Université de Sherbrooke', 'class': 'OTHER'}, 'collaborators': [{'name': 'Lucine', 'class': 'INDUSTRY'}, {'name': 'Centre for Research of CHUS (CRCHUS)', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}