Clinical trial · Interventional
Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer Pain | — | UNRESOLVED | — |
| Chronic Pain | — | UNRESOLVED | — |
| Neuropathic Pain | — | UNRESOLVED | — |
| Pain Assessment | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Multimodal AI-Based Pain Assessment | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- AI-Based Pain Assessment in Chronic Pain Patients
- description
- 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.
- interventionNames
- Diagnostic Test: Multimodal AI-Based Pain Assessment
Primary outcomes (6)
- measure
- Accuracy of AI models in classifying chronic pain
- timeFrame
- From Day 0 (baseline) to Day 30 (follow-up)
- description
- Accuracy will be calculated to evaluate how well supervised machine learning and deep learning models can correctly classify the presence of chronic pain using multimodal data (e.g., biosignals, facial thermography, video, and audio).
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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).
References
Publications (17)
- BACKGROUNDTreede RD, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, Cohen M, Evers S, Finnerup NB, First MB, Giamberardino MA, Kaasa S, Korwisi B, Kosek E, Lavand'homme P, Nicholas M, Perrot S, Scholz J, Schug S, Smith BH, Svensson P, Vlaeyen JWS, Wang SJ. Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). Pain. 2019 Jan;160(1):19-27. doi: 10.1097/j.pain.0000000000001384. PMID 30586067
- BACKGROUNDCascella M, Di Gennaro P, Crispo A, Vittori A, Petrucci E, Sciorio F, Marinangeli F, Ponsiglione AM, Romano M, Ovetta C, Ottaiano A, Sabbatino F, Perri F, Piazza O, Coluccia S. Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain. BMC Palliat Care. 2024 Aug 3;23(1):198. doi: 10.1186/s12904-024-01526-z. PMID 39097739
- BACKGROUNDMachova K, Szaboova M, Paralic J, Micko J. Detection of emotion by text analysis using machine learning. Front Psychol. 2023 Sep 20;14:1190326. doi: 10.3389/fpsyg.2023.1190326. eCollection 2023. PMID 37799520
- BACKGROUNDAlbashayreh A, Bandyopadhyay A, Zeinali N, Zhang M, Fan W, Gilbertson White S. Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives. JCO Clin Cancer Inform. 2024 Aug;8:e2300235. doi: 10.1200/CCI.23.00235. PMID 39116379
- BACKGROUNDLotsch J, Ultsch A, Mayer B, Kringel D. Artificial intelligence and machine learning in pain research: a data scientometric analysis. Pain Rep. 2022 Nov 3;7(6):e1044. doi: 10.1097/PR9.0000000000001044. eCollection 2022 Nov-Dec. PMID 36348668
- BACKGROUNDCascella M, Schiavo D, Cuomo A, Ottaiano A, Perri F, Patrone R, Migliarelli S, Bignami EG, Vittori A, Cutugno F. Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives. Pain Res Manag. 2023 Jun 28;2023:6018736. doi: 10.1155/2023/6018736. eCollection 2023.