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

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study summary

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

Primary outcome measure(s)

• Accuracy of AI models in classifying chronic pain — From Day 0 (baseline) to Day 30 (follow-up)
  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).
• Sensitivity of AI models in classifying chronic pain — From Day 0 to Day 30
  Sensitivity (true positive rate) will be computed to determine the model's ability to correctly identify patients experiencing chronic pain. Unit of measure: Sensitivity (%)
• Specificity of AI models in classifying chronic pain — From Day 0 to Day 30
  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 (%)
• Precision of AI models in classifying chronic pain — From Day 0 to Day 30
  Precision (positive predictive value) will be calculated to assess the proportion of correct positive predictions among all positive classifications. Unit of measure: Precision (%)
• F1-score of AI models in classifying chronic pain — From Day 0 to Day 30
  F1-score, the harmonic mean of precision and sensitivity, will be used to assess overall model performance, especially in the presence of class imbalance. Unit of measure: F1-score (numeric value)
• AUC-ROC of AI models in classifying chronic pain — From Day 0 to Day 30
  The area under the receiver operating characteristic curve (AUC-ROC) will be used to evaluate the model's ability to discriminate between pain and no-pain conditions across thresholds. Unit of measure: AUC-ROC (numeric value from 0 to 1)

Trial sites (1)

Other trials for the same condition