Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 200
- 试验地点
- 2
- 主要终点
- Accuracy of AI models in classifying chronic pain
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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).
结局指标
主要结局
Accuracy of AI models in classifying chronic pain
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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
时间窗: 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)
次要结局
- Change in Patient Global Impression of Change (PGIC) score(From Day 0 to Day 30)
- Change in Brief Pain Inventory (BPI) interference score(From Day 0 to Day 30)
- Correlation between analgesic treatments and pain intensity (NRS)(From Day 0 to Day 30)
研究者
Valentina Cerrone
Study Coordinator
Azienda Ospedaliera OO.RR. S. Giovanni di Dio e Ruggi D'Aragona