RecruitingNot ApplicableNCT07038434

Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study


Sponsor

Valentina Cerrone

Enrollment

200 participants

Start Date

May 6, 2025

Study Type

INTERVENTIONAL

Conditions

Summary

This single-center, non-profit, observational-interventional study aims to develop artificial intelligence (AI) models for the automatic assessment of chronic pain (APA - Automatic Pain Assessment). The study will enroll adult patients with chronic pain of various origins (oncologic and non-oncologic). Participants will undergo multidimensional evaluations that include clinical assessments, self-report questionnaires, bio-signal collection (e.g., EEG, EDA, HRV, GSR, PPG), and facial expression analysis via infrared thermography and video recordings. The primary objective is to calibrate and test machine learning and deep learning models to recognize and predict the presence and severity of pain using multimodal data inputs. Secondary objectives include evaluating the effectiveness of pain treatments, assessing quality of life, and developing a standardized APA dataset for future research. All data collection procedures are non-invasive and safe, and include tools like wearable sensors and standardized neurocognitive tests. The study is approved by the Italian Ethics Committee (Comitato Etico Territoriale Campania 2) and complies with GDPR and EU AI regulations.


Eligibility

Min Age: 18 Years

Plain Language Summary

Simplified for easier understanding

This clinical trial is studying Multimodal AI-Based Pain Assessment for people with cancer pain, chronic pain, and other related conditions. The study is currently recruiting participants at 1 location.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

DIAGNOSTIC_TESTMultimodal AI-Based Pain Assessment

A non-invasive, multimodal diagnostic procedure combining self-reported pain scales (NRS, DN-4, BPI), wearable biosignal acquisition (EDA, EMG, HRV, EEG), facial thermography (HIRA system), video-based facial expression analysis, linguistic interview, and the Stroop Test. Data are used to train and validate machine learning models for automatic pain assessment in chronic pain patients.


Locations(1)

Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona

Salerno, Italy, Italy

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NCT07038434


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