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

Sponsor
Valentina Cerrone
Study ID
NCT07038434
Status
Recruiting
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Conditions

  • Cancer Pain
  • Chronic Pain
  • Neuropathic Pain
  • Pain Assessment

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • Multimodal AI-Based Pain Assessment — DIAGNOSTIC_TEST
    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.

Study Details

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.

Key Dates

First listed
Jun 26, 2025
Start date
May 6, 2025
Status verified
Jun 2025
Primary completion
Dec 31, 2025
Completion
Jan 31, 2026

Study Design

Enrollment
200 participants (estimated)
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC

Arms

  • Experimental: AI-Based Pain Assessment in Chronic Pain Patients
    Participants with chronic pain will undergo a multimodal, non-invasive diagnostic assessment including self-reported pain questionnaires (NRS, DN-4, BPI), wearable biosignal acquisition (EEG, EMG, EDA, HRV), facial thermography using the HIRA system, video-based facial expression analysis, linguistic evaluation, and the Stroop Test. These data will be used to develop and validate machine learning models for automatic pain assessment.

Primary Outcome Measure

Accuracy of AI models in classifying chronic pain [ Time Frame: From Day 0 (baseline) to Day 30 (follow-up) ]

Central Contacts

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