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

ConditionsChronic Pain Cancer Pain Neuropathic Pain Pain Assessment

Trial statusRecruiting

Trial phaseNot applicable

Trial typeInterventional

Biological sexAll

Age18+

SponsorValentina Cerrone

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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.

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)

Disqualifiers

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.

Trial design

Design model

Single group

Treatments tested in this trial

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.

Treatment groups

200 Participants

are divided into 1 treatment group

Group A: AI-Based Pain Assessment in Chronic Pain PatientsExperimental treatment 1 intervention

Trial outcomes

Primary outcomes

Accuracy of AI models in classifying chronic pain

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).

Time frame

From Day 0 (baseline) to Day 30 (follow-up)

Sensitivity of AI models in classifying chronic pain

Sensitivity (true positive rate) will be computed to determine the model's ability to correctly identify patients experiencing chronic pain. Unit of measure: Sensitivity (%)

Time frame

From Day 0 to Day 30

Specificity of AI models in classifying chronic pain

Specificity (true negative rate) will be computed to assess the model's ability to correctly identify patients who are not experiencing chronic pain. Unit of measure: Specificity (%)

Time frame

From Day 0 to Day 30

Precision of AI models in classifying chronic pain

Precision (positive predictive value) will be calculated to assess the proportion of correct positive predictions among all positive classifications. Unit of measure: Precision (%)

Time frame

From Day 0 to Day 30

Secondary outcomes

Change in Patient Global Impression of Change (PGIC) score

This outcome will measure patients' perceived improvement in their condition using the PGIC scale. Unit of measure: Score on a 7-point Likert scale (1 = No change to 7 = Very much improved)

Time frame

From Day 0 to Day 30

Change in Brief Pain Inventory (BPI) interference score

This outcome will measure how much pain interferes with daily functioning, using the BPI interference subscale. Unit of measure: Score from 0 (no interference) to 10 (complete interference)

Time frame

From Day 0 to Day 30

Correlation between analgesic treatments and pain intensity (NRS)

The outcome will assess the correlation between the type and frequency of analgesic treatments and changes in pain intensity, measured with the Numeric Rating Scale (NRS). Unit of measure: Pearson correlation coefficient (r), NRS scores from 0 to 10

Time frame

From Day 0 to Day 30

Other outcomes

Creation of a structured multimodal dataset for AI-based pain research

A standardized and anonymized dataset will be developed from collected multimodal inputs (biosignals, thermography, facial videos, linguistic data, questionnaires) to enable future research. Unit of measure: Dataset availability (Yes/No)

Time frame

From Day 0 to Day 30