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

Обзор

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.

Подробное описание

This study, titled "Refining mUltiple artificial intelliGence strateGies for automatic pain assessment Investigations" (RUGGI), explores the integration of AI in chronic pain evaluation. Pain is a multidimensional and subjective experience, and conventional assessment methods often rely solely on self-reported scales. This introduces the risk of over- or under-treatment. To overcome this limitation, the study leverages multimodal data-including physiological signals, facial expressions, and linguistic analysis-to build models capable of objectively assessing pain intensity and characteristics.

The primary aim is to calibrate predictive models (e.g., Support Vector Machines, Random Forest, Convolutional Neural Networks, YOLO architectures, and MLPs) that can recognize pain patterns using supervised and unsupervised learning. Bio-signals (EEG, HRV, GSR, EMG), infrared thermography (HIRA system), and prosodic-linguistic features will be analyzed. Data will be collected during structured timepoints: baseline (rest), Stroop test execution, and follow-up.

Patients are recruited based on chronic pain diagnosis per IASP and ICD-11 criteria. Inclusion criteria include age ≥18 and informed consent. The study foresees a target enrollment of approximately 200 patients within 6 months. Data will be processed following a rigorous AI pipeline, including preprocessing, feature extraction, dimensionality reduction, and cross-validation (k-fold with grid search optimization). Outcome measures include the Area Under the Curve (AUC), sensitivity, specificity, F1 score, and model explainability (via SHAP, LIME).

Secondary outcomes include assessing patient-reported quality of life, evaluating analgesic strategies, and generating a public-use APA dataset. All procedures are compliant with Good Clinical Practice (GCP), GDPR, and EU Artificial Intelligence Act (Reg. 2024/1689). The study is conducted at the University Hospital "San Giovanni di Dio e Ruggi d'Aragona" in Salerno, Italy.

Вмешательства

• Диагностический тест Multimodal 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.

Первичные конечные точки

• Accuracy of AI models in classifying chronic pain [Срок оценки: From Day 0 (baseline) to Day 30 (follow-up)]
• Sensitivity of AI models in classifying chronic pain [Срок оценки: From Day 0 to Day 30]
• Specificity of AI models in classifying chronic pain [Срок оценки: From Day 0 to Day 30]
• Precision of AI models in classifying chronic pain [Срок оценки: From Day 0 to Day 30]
• F1-score of AI models in classifying chronic pain [Срок оценки: From Day 0 to Day 30]
• AUC-ROC of AI models in classifying chronic pain [Срок оценки: From Day 0 to Day 30]

Вторичные конечные точки (3)

• Change in Patient Global Impression of Change (PGIC) score [Срок оценки: From Day 0 to Day 30]
• Change in Brief Pain Inventory (BPI) interference score [Срок оценки: From Day 0 to Day 30]
• Correlation between analgesic treatments and pain intensity (NRS) [Срок оценки: From Day 0 to Day 30]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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).

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Италия · 1 центр

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

Публикации

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• Cascella 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
• Machova 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
• Albashayreh 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
• Lotsch 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
• Cascella 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. PMID 37416623
• Nicholas MK. The biopsychosocial model of pain 40 years on: time for a reappraisal? Pain. 2022 Nov 1;163(Suppl 1):S3-S14. doi: 10.1097/j.pain.0000000000002654. No abstract available. PMID 36252231
• Kutafina E, Becker S, Namer B. Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods. Front Netw Physiol. 2023 Feb 10;3:1099282. doi: 10.3389/fnetp.2023.1099282. eCollection 2023. PMID 36926544

Идентификаторы

NCT: NCT07038434 · AOURUGGI-0012506-2025

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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