Chronic PainRUGGINCT07038434
Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
A study testing Multimodal AI-Based Pain Assessment in people with Chronic Pain and Cancer Pain, run by Valentina Cerrone at 1 site in Italy.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chronic Pain, Cancer Pain, Neuropathic Pain
- What is being tested?
- Multimodal AI-Based Pain Assessment
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteItaly
- Study dates
- 6 May 2025to Jan 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Valentina Cerrone, RN, MSc contact
Italy1 site, 1 recruiting
- Recruiting
Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona
Salerno, Italy, 84131
Marco Cascella, MD, PhD, +39 089672428, mcascella@unisa.it
Valentina Cerrone, RN, MSc, valentina.cerrone@sangiovannieruggi.it
About this study
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.
Read the detailed description
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.
- Conditions
- Chronic Pain, Cancer Pain, Neuropathic Pain, Pain Assessment
- Being tested
- Multimodal AI-Based Pain Assessment
- Keywords
- Artificial Intelligence, Machine Learning, Deep Learning, Automatic Pain Assessment, Facial Expression Analysis, Natural Language Processing, Stroop Test, Bio-signals, Chronic Pain
- Medical subject headings
- Chronic Pain, Cancer Pain, Neuralgia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal AI-Based Pain AssessmentDiagnostic test
Also called Automatic Pain Assessment, AI Pain Evaluation
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 group | Role | What they receive |
|---|---|---|
| AI-Based Pain Assessment in Chronic Pain Patients | Experimental | Participants with chronic pain will undergo a multimodal, non-invasive diagnostic assessment including self-reported pain questionnaires (NRS, DN-4, BPI), wearable biosignal acquisition (EEG, EMG, EDA, HRV), facial thermography using the HIRA system, video-based facial expression analysis, linguistic evaluation, and the Stroop Test. These data will be used to develop and validate machine learning models for automatic pain assessment. Receives: Diagnostic Test: Multimodal AI-Based Pain Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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 (%)
Measured: 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 (%)
Measured: 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 (%)
Measured: From Day 0 to Day 30
F1-score of AI models in classifying chronic pain
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)
Measured: From Day 0 to Day 30
AUC-ROC of AI models in classifying chronic pain
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)
Measured: From Day 0 to Day 30
Secondary outcomes (3)
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)
Measured: 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)
Measured: 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
Measured: From Day 0 to Day 30
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jun 2025
- Study start6 May 2025
- Primary completionDec 2025 (estimated)
- Study completionJan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (17)Papers the sponsor has linked to this study.
- Other trials for Chronic PainStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- All participants receive the same diagnostic evaluation protocol, including clinical pain assessment, wearable bio-signal monitoring, neurocognitive testing, facial expression analysis, and language processing. The study is designed as a single-arm exploratory diagnostic protocol.
- Masking
- None (open label)
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Valentina Cerrone
- Sponsor type
- University, hospital or other
- Collaborators
- University of Salerno, Italy, Federico II University
- Responsible party
- Sponsor-investigator, Valentina Cerrone, Azienda Ospedaliera OO.RR. S. Giovanni di Dio e Ruggi D'Aragona
- Investigators
- Marco Cascella, MD, PhD, Principal investigator, University of Salerno
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07038434
- Sponsor's protocol ID
- AOURUGGI-0012506-2025
- Acronym
- RUGGI
- Status last verified
- Jun 2025
- First submitted
- 19 May 2025
- Last updated
- 26 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Publications
Treede RD, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, Cohen M, Evers S, Finnerup NB, First MB, Giamberardino MA, Kaasa S, Korwisi B, Kosek E, Lavand'homme P, Nicholas M, Perrot S, Scholz J, Schug S, Smith BH, Svensson P, Vlaeyen JWS, Wang SJ. Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). Pain. 2019 Jan;160(1):19-27. doi: 10.1097/j.pain.0000000000001384.
PubMed 30586067Cascella 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.
PubMed 39097739Machova 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.
PubMed 37799520Albashayreh 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.
PubMed 39116379Lotsch 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.
PubMed 36348668Cascella 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.
PubMed 37416623Nicholas 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.
PubMed 36252231Kutafina 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.
PubMed 36926544Lang VA, Lundh T, Ortiz-Catalan M. Mathematical and Computational Models for Pain: A Systematic Review. Pain Med. 2021 Dec 11;22(12):2806-2817. doi: 10.1093/pm/pnab177.
PubMed 34051102Kaplan CM, Kelleher E, Irani A, Schrepf A, Clauw DJ, Harte SE. Deciphering nociplastic pain: clinical features, risk factors and potential mechanisms. Nat Rev Neurol. 2024 Jun;20(6):347-363. doi: 10.1038/s41582-024-00966-8. Epub 2024 May 16.
PubMed 38755449Clauw DJ. From fibrositis to fibromyalgia to nociplastic pain: how rheumatology helped get us here and where do we go from here? Ann Rheum Dis. 2024 Oct 21;83(11):1421-1427. doi: 10.1136/ard-2023-225327.
PubMed 39107083Cohen SP, Vase L, Hooten WM. Chronic pain: an update on burden, best practices, and new advances. Lancet. 2021 May 29;397(10289):2082-2097. doi: 10.1016/S0140-6736(21)00393-7.
PubMed 34062143Rahman S, Kidwai A, Rakhamimova E, Elias M, Caldwell W, Bergese SD. Clinical Diagnosis and Treatment of Chronic Pain. Diagnostics (Basel). 2023 Dec 18;13(24):3689. doi: 10.3390/diagnostics13243689.
PubMed 38132273Zimmer Z, Fraser K, Grol-Prokopczyk H, Zajacova A. A global study of pain prevalence across 52 countries: examining the role of country-level contextual factors. Pain. 2022 Sep 1;163(9):1740-1750. doi: 10.1097/j.pain.0000000000002557. Epub 2021 Dec 15.
PubMed 35027516Cascella M, Ponsiglione AM, Santoriello V, Romano M, Amato F, Sabbatino F, Pepe S, Piazza O. Refining multiple artificial intelligence strategies for automatic pain assessment investigations (RUGGI Study): A study protocol. Eur J Anaesthesiol Intensive Care. 2026 May 15;5(3):e0111. doi: 10.1097/EA9.0000000000000111. eCollection 2026 Jun.
PubMed 42244836
Study at a glance
From the registry record, last updated by the sponsor on 26 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
- ClinicalTrials.gov ID
- NCT07038434
- Acronym / sponsor ID
- RUGGI / AOURUGGI-0012506-2025
- Conditions
- Chronic Pain, Cancer Pain, Neuropathic Pain, Pain Assessment
- Keywords
- Artificial Intelligence, Machine Learning, Deep Learning, Automatic Pain Assessment, Facial Expression Analysis, Natural Language Processing, Stroop Test, Bio-signals
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Valentina CerroneUniversity, hospital or other
- Collaborators
- University of Salerno, Italy, Federico II University
- Study start
- 6 May 2025 (actual)
- Primary completion
- Dec 2025 (estimated)
- Study completion
- Jan 2026 (estimated)
- First posted
- 26 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- All participants receive the same diagnostic evaluation protocol, including clinical pain assessment, wearable bio-signal monitoring, neurocognitive testing, facial expression analysis, and language processing. The study is designed as a single-arm exploratory diagnostic protocol.
| Arm | Type | Receives |
|---|---|---|
| AI-Based Pain Assessment in Chronic Pain Patients | Experimental | Diagnostic Test: Multimodal AI-Based Pain AssessmentParticipants with chronic pain will undergo a multimodal, non-invasive diagnostic assessment including self-reported pain questionnaires (NRS, DN-4, BPI), wearable biosignal acquisition (EEG, EMG, EDA, HRV), facial thermography using the HIRA system, video-based facial expression analysis, linguistic evaluation, and the Stroop Test. These data will be used to develop and validate machine learning models for automatic pain assessment. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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)
Key exclusion criteria (5 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Multimodal AI-Based Pain AssessmentAlso: Automatic Pain Assessment, AI Pain EvaluationA 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. | Diagnostic test | AI-Based Pain Assessment in Chronic Pain Patients |
Primary outcome
Accuracy of AI models in classifying chronic pain
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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)
3 secondary outcomes
Change in Patient Global Impression of Change (PGIC) score
Time frame: From Day 0 to Day 30
Change in Brief Pain Inventory (BPI) interference score
Time frame: From Day 0 to Day 30
Correlation between analgesic treatments and pain intensity (NRS)
Time frame: From Day 0 to Day 30
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jun 2025.
- Background: PubMed 30586067Treede RD, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, Cohen M, Evers S, Finnerup NB, First MB, Giamberardino MA, Kaasa S, Korwisi B, Kosek E, Lavand'homme P, Nicholas M, Perrot S, Scholz J, Schug S, Smith BH, Svensson P, Vlaeyen JWS, Wang SJ. Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). Pain. 2019 Jan;160(1):19-27. doi: 10.1097/j.pain.0000000000001384.
- Background: PubMed 39097739Cascella 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.
- Background: PubMed 37799520Machova 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.
- Background: PubMed 39116379Albashayreh 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.
- Background: PubMed 36348668Lotsch 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.
- Background: PubMed 37416623Cascella 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.
- Background: PubMed 36252231Nicholas 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.
- Background: PubMed 36926544Kutafina 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.
- Background: PubMed 34051102Lang VA, Lundh T, Ortiz-Catalan M. Mathematical and Computational Models for Pain: A Systematic Review. Pain Med. 2021 Dec 11;22(12):2806-2817. doi: 10.1093/pm/pnab177.
- Background: PubMed 38755449Kaplan CM, Kelleher E, Irani A, Schrepf A, Clauw DJ, Harte SE. Deciphering nociplastic pain: clinical features, risk factors and potential mechanisms. Nat Rev Neurol. 2024 Jun;20(6):347-363. doi: 10.1038/s41582-024-00966-8. Epub 2024 May 16.
- Background: PubMed 39107083Clauw DJ. From fibrositis to fibromyalgia to nociplastic pain: how rheumatology helped get us here and where do we go from here? Ann Rheum Dis. 2024 Oct 21;83(11):1421-1427. doi: 10.1136/ard-2023-225327.
- Background: PubMed 34062143Cohen SP, Vase L, Hooten WM. Chronic pain: an update on burden, best practices, and new advances. Lancet. 2021 May 29;397(10289):2082-2097. doi: 10.1016/S0140-6736(21)00393-7.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Valentina Cerrone, RN, MSc contact
Marco Cascella, MD, PhD principal investigator
University of Salerno
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Multimodal AI-Based Pain Assessment in people with Chronic Pain and Cancer Pain, run by Valentina Cerrone at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 26 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Marco Cascella, MD, PhD (+39 089 672428, mcascella@unisa.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of AI models in classifying chronic pain (time frame: From Day 0 (baseline) to Day 30 (follow-up)). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07038434, which also lists 17 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Jun 2025.