Evidence map›Paper›PMID 42244836›Full record

ArticleEuropean journal of anaesthesiology and intensive care2026

Refining multiple artificial intelligence strategies for automatic pain assessment investigations (RUGGI Study): A study protocol.

Marco Cascella, Alfonso Maria Ponsiglione, Vittorio Santoriello, Maria Romano, Francesco Amato, Francesco Sabbatino, Stefano Pepe, Ornella Piazza

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of anaesthesiology and intensive care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07038434 (Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07038434 narecruitingnot on this map

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

TypeinterventionalSponsorValentina CerroneRan2025 to 2026Enrolled200ConditionsChronic Pain, Cancer Pain, Neuropathic Pain, Pain AssessmentArmsMultimodal AI-Based Pain Assessment
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Marco CascellaFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).
Alfonso Maria PonsiglioneFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).
Vittorio SantorielloFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).
Maria RomanoFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).
Francesco AmatoFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).
Francesco SabbatinoFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).
Stefano PepeFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).
Ornella PiazzaFrom the Anesthesia, Intensive Care and Pain Medicine, Department of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana', University of Salerno, Baronissi (MC, OP), Department of Electrical Engineering and Information Technology, University of Naples 'Federico II', Naples (AMP, VS, MR, FA) and Oncology, Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Salerno, Italy (FS, SP).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic pain is a complex, multidimensional condition that severely impairs patients' quality of life. As conventional techniques for evaluating pain are based on subjective self-reporting, these approaches have crucial drawbacks, especially for individuals with communication difficulties. Artificial intelligence (AI) provides the opportunity to complement subjective self-reports through multimodal, data-informed analysis to enhance real-time pain assessment and care.

objectiveTo develop, calibrate and validate AI models for automatic pain assessment (APA) in adult patients by merging physiological, behavioural and clinical data and, consequently, complement patient-reported information and support more personalised and effective pain management.

designProspective, single-centre, noninterventional study.

settingUniversity of Salerno Hospital, Italy. PATIENTS AND

participantsAdult patients (>18 years) with chronic primary or secondary pain (oncologic and nononcologic), able to provide their informed consent. The main exclusion criteria are severe psychiatric or cognitive disorders and treatment with psychotropic medications. PRIMARY OUTCOME MEASURES: Predictive performance of AI models (sensitivity, specificity, area under the receiver operating characteristic curve, AUC-ROC) for automatic pain assessment based on collected multimodal data. SECONDARY OUTCOMES: Quality-of-life evaluation, analgesic treatment monitoring, development and analysis of a multidimensional dataset for APA and identification of correlations between clinical and physiological variables.

resultsN/A (study ongoing).

conclusionsThis study will provide essential data for developing and validating integrated AI tools for objective, multidimensional pain assessment, with potential future clinical and therapeutic applications.

trial registrationClinicalTrials.gov Identifier: NCT07038434.

Identifiers

PMID42244836
PMCPMC13232929

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.