Analysis of Facial Expressions for Pain Recognition in Fibromyalgia: Using Artificial Intelligence and Biomarkers (Fibromyalgia)

November 17, 2025 updated by: Alessandra Hubner de Souza, Faculdade de Ciências Médicas de Minas Gerais

Analysis of Facial Expressions for Pain Recognition: Using Artificial Intelligence and Biomarkers as a Pain Diagnostic Tool in Fibromyalgia, a Pilot Study

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.

Study Overview

Status

Recruiting

Conditions

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.

Study Type

Observational

Enrollment (Estimated)

122

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Brazil
    • Minas Gerais
      • Belo Horizonte, Minas Gerais, Brazil, 30130110
        • Recruiting
        • Outpatient Faculty Medical Sciences

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Patients diagnosed with fibromyalgia
The aim is to follow fibromyalgia patients over a period of months to analyze pain intensity and identify molecular markers associated with the condition. The study encompasses facial analysis techniques and molecular markers, along with artificial intelligence tools, to quantify pain and understand the underlying mechanisms of fibromyalgia. The methodology is predominantly quantitative, focused on collecting and analyzing objective data on pain and associated markers.
Patients without a diagnosis of fibromyalgia
The aim is to follow patients with pain over a period of months to analyze the intensity and identify molecular markers associated with the condition. The study encompasses facial analysis techniques and molecular markers, along with artificial intelligence tools, to quantify pain and understand the underlying mechanisms of fibromyalgia. The methodology is predominantly quantitative, focused on collecting and analyzing objective data on pain and associated markers.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Use the artificial intelligence tool to analyze the facial expression of patients with fibromyalgia in order to recognize pain.
Time Frame: 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.
Use the artificial intelligence tool to analyze the facial expression of patients with fibromyalgia in order to recognize pain. 61 patients with fibromyalgia will be compared with 61 patients without fibromyalgia.
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Biochemical analysis of biomarkers in the detection of pain in fibromyalgia.
Time Frame: Estimated collection and analysis time: 5 hours of durability
122 samples containing 1ml of saliva will be collected in order to check for changes in oxidative stress and nociceptive markers (by spectrophotometry and ELISA).
Estimated collection and analysis time: 5 hours of durability

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Faculdade de Ciências Médicas de Minas Gerais

Collaborators

Conselho Nacional de Desenvolvimento Científico e Tecnológico
Fundação de Amparo à Pesquisa do estado de Minas Gerais

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 17, 2025

Primary Completion (Estimated)

February 1, 2026

Study Completion (Estimated)

September 1, 2026

Study Registration Dates

First Submitted

December 19, 2024

First Submitted That Met QC Criteria

February 3, 2025

First Posted (Actual)

February 6, 2025

Study Record Updates

Last Update Posted (Actual)

November 19, 2025

Last Update Submitted That Met QC Criteria

November 17, 2025

Last Verified

February 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CMMG

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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