Clinical trial NCT06813352: Analysis of Facial Expressions for Pain Recognition in Fibromyalgia: Using Artificial Intelligence and Biomarkers
Official title: Analysis of Facial Expressions for Pain Recognition: Using Artificial Intelligence and Biomarkers as a Pain Diagnostic Tool in Fibromyalgia, a Pilot Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Fibromyalgia (FM)
Pain
Catastrophizing Pain
Keywords: Fibromyalgia
Artificial intelligence
Molecular biomarkers
Catastrophism
Facial expression
Lead sponsor: Faculdade de Ciências Médicas de Minas Gerais
Sponsor class: OTHER
Collaborators: name: Fundação de Amparo à Pesquisa do estado de Minas Gerais
name: Conselho Nacional de Desenvolvimento Científico e Tecnológico
First posted: 2025-02-06
Enrollment: 122
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: PROBABILITY_SAMPLE
Study population: The study will be carried out with adult patients aged between 18 and 65 diagnosed with fibromyalgia at the outpatient clinic of the Faculdade Ciências Médicas de Minas Gerais and Clínica Ampla de Reumatologia in Belo Horizonte. The inclusion criteria were patients with no diagnosed cognitive deficit and who were willing to take part in the study. The exclusion criteria are patients who use medication that can affect anxiety or depression or who are unable to understand the instructions. Thus, once the patients agree to participate, they will be asked to sign the Informed Consent Form (ICF), which will provide detailed information about the nature of the study, the criteria for participation, the possible risks and benefits involved.

Brief summary: Fibromyalgia (FM) is a chronic musculoskeletal pain syndrome with characteristics of generalized body pain, low pain threshold, tenderness and stiffness in muscles, tendons and joints. The assessment of pain in this condition is a challenge due to its subjective nature. A promising approach to assessing pain intensity is facial expression analysis, which can serve as an objective indicator. In addition, research seeks to identify molecular molecular markers to quantify pain. However, the lack of a standardized system has made it difficult to identify reliable markers. In summary, the search for objective methods of assessing pain in fibromyalgia is essential in order to develop more effective more effective treatments. Facial expression analysis and the investigation of molecular markers are promising ways of quantifying pain intensity more accurately and intensity of pain more accurately and reliably in fibromyalgia.

Detailed description: Introduction:

Fibromyalgia (FM) is a chronic syndrome characterized by diffuse musculoskeletal pain, fatigue and sleep disturbances, with a major impact on quality of life. Due to the subjectivity of pain assessment, the development of objective methods is essential. This study explores the use of artificial intelligence (AI) in the analysis of facial expressions, combined with the investigation of molecular markers, as an innovative and quantitative approach to pain assessment in patients with FM.

Objective:

To validate the application of an AI tool combined with facial expression analysis and molecular biomarker research to measure pain intensity in FM patients.

Methodology:

An observational cohort study was carried out with 122 participants, divided into two groups: patients with FM (n=61) and without FM (n=61). Data collection included:

1. Facial expression recording: A convolutional neural network algorithm was used to analyze facial patterns associated with pain.
2. Biological samples: 1mL of saliva will be collected from each participant using the salivette method and processed to extract DNA, RNA and plasma proteins. The proteins will be quantified by ELISA and the genes associated with FM will be analyzed by RT-qPCR.
3. Clinical Questionnaires: Psychometric instruments such as the Visual Analogue Scale (VAS) and the Generalized Pain Index (GDI) were used to validate the results.
4. Statistical analysis: The data was analyzed using Kappa and Bland-Altman correlations to assess the agreement between the AI methods and the questionnaires, with a significance level of p\<0.05.

The AI algorithm will use consistent facial patterns correlating them to the reported pain intensity, in agreement (Kappa=0.82) with the results of the clinical scales.The molecular markers analyzed are expected to show significant differences between the groups, with increased expression of inflammatory proteins in FM patients (p\<0.05). The integration of facial and molecular analysis aims to amplify the accuracy of pain intensity classification.

This approach represents a promising advance in the diagnosis and management of the syndrome, contributing to personalized therapies and improving patients' quality of life.

Eligibility criteria: Inclusion Criteria:

* The inclusion criteria are patients with no diagnosed cognitive deficit and who are willing to take part in the study.

Exclusion Criteria:

* Exclusion criteria are patients who use medication that can affect anxiety or depression or inability to understand the instructions.

Primary outcomes:
measure: Use the artificial intelligence tool to analyze the facial expression of patients with fibromyalgia in order to recognize pain.
timeFrame: The estimated time for the anamnesis is one hour, during which time biological samples will be taken and each patient's facial expressions will be recorded on camera.
Secondary outcomes:
measure: Biochemical analysis of biomarkers in the detection of pain in fibromyalgia.
timeFrame: Estimated collection and analysis time: 5 hours of durability
Other outcomes:
Locations:
city: Belo Horizonte
country: Brazil
facility: Outpatient Faculty Medical Sciences
state: Minas Gerais
Study officials:
References:
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