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.

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.

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).

Individual participant data (IPD) will not be shared due to the sensitive nature of biometric and video-derived data, including facial thermography and audio recordings. Although all data are anonymized, there remains a potential risk of re-identification through multimodal signals. Additionally, no specific provisions for data sharing were included in the original informed consent approved by the ethics committee.