Refining multiple AI strategies for automatic pain assessment (RUGGI)
Official title: Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
Run by Azienda Ospedaliera OO.RR. S. Giovanni di Dio e Ruggi D'Aragona · NCT07038434 · Data updated 2026-11-12
This project tests whether AI models using wearable sensors, facial video/thermography, and physiological signals can detect and estimate pain in adults with chronic or cancer-related pain.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 200 (estimated) |
| Sponsor | Azienda Ospedaliera OO.RR. S. Giovanni di Dio e Ruggi D'Aragona Academic / other |
| Locations | 1 site (Salerno, Italy) |
| Status | Recruiting |
| Started | May 6, 2025 |
| Main results expected | December 2025 |
| Study ends | January 2026 |
| Registry record updated | June 26, 2025 |
| Trial ID | NCT07038434 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (≥18) with chronic primary, secondary non-cancer, or cancer-related pain per IASP/ICD-11 criteria who can understand procedures, consent, and attend onsite recording sessions are ideal candidates.
✕Probably not a fit
People on psychotropic medications or with active psychiatric disorders, substance abuse, pregnancy, cognitive impairment, or those unable to travel to the study site are excluded and unlikely to benefit from participation.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This single-center, observational-interventional project will enroll about 200 adults with chronic pain from oncologic and non-oncologic causes and collect multimodal, non-invasive data including EEG, HRV, GSR/EDA, EMG, PPG, infrared thermography, facial video, and self-report questionnaires. Recordings occur at structured timepoints (baseline rest, during a Stroop task, and follow-up) with wearable sensors and synchronized video to capture physiological and behavioral responses. Data will be preprocessed, features extracted, and machine learning pipelines (SVMs, random forests, CNNs, YOLO, MLPs) trained with cross-validation and grid-search optimization to recognize presence and severity of pain. The project also intends to produce a standardized, anonymized APA dataset and explore links between objective signals, treatment response, and quality of life.
Why it matters
Potential benefit: If successful, the approach could give clinicians more objective, continuous information about pain to help tailor treatments and reduce under- or over-treatment.
How similar studies have gone: Prior work using facial expressions and physiological signals for pain detection has shown promising but inconsistent results, and fully validated multimodal AI models for chronic pain remain limited.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Adults (≥18 years old) with chronic pain, defined according to IASP and ICD-11 as pain that persists or recurs for more than three months. * Diagnosed with either: * Chronic primary pain (e.g., fibromyalgia, irritable bowel syndrome, chronic headaches) * Chronic secondary non-cancer pain (e.g., low back pain, osteoarthritis, post-surgical pain) * Chronic cancer-related pain (due to cancer or its treatment) * Ability to understand the study procedures and provide written informed consent. Exclusion Criteria: * Current treatment with psychotropic drugs or presence of active psychiatric disorders (e.g., psychosis, major depression). * Known history of alcohol or substance abuse. * Pregnancy or breastfeeding. * Age under 18 years. * Inability to provide informed consent (e.g., due to cognitive impairment).
Where it's running
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Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona
Salerno, Italy, Italy Recruiting
Study contacts
- Principal investigator: Marco Cascella, MD, PhD — University of Salerno
- Study coordinator: Marco Cascella, MD, PhD
- Email: mcascella@unisa.it
- Phone: +39 089 672428
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.