Clinical trial · Observational
Real-Time AI During Pancreatoscopy for Detection of Pancreatic Neoplasia
Real-Time Application of a Validated Artificial Intelligence Model During Digital Per-Oral Pancreatoscopy for Identification of Pancreatic Neoplastic Lesions and IPMN: A Prospective Pilot Diagnostic Accuracy Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 10, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260910-000001
Summary
Brief summary (as posted)
This prospective pilot study will evaluate the diagnostic performance of a previously validated artificial intelligence (AI) model when applied in real time during digital per-oral pancreatoscopy (POPS). The study will include adults undergoing clinically indicated pancreatoscopy for suspected or known intraductal papillary mucinous neoplasm (IPMN), indeterminate pancreatic-duct abnormalities, or preoperative assessment of IPMN extent. During the procedure, the endoscopist will first record a visual assessment while the AI system is hidden. The AI overlay will then be activated during the same pancreatoscopy examination, and its findings will be recorded independently. AI and endoscopist assessments will be compared with a prespecified reference standard based on surgical histopathology when available or tissue sampling and clinical/imaging follow-up when surgery is not performed. The primary objective is to estimate the sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of real-time AI for identifying high-grade dysplasia or invasive carcinoma. The study is designed as a pilot to assess feasibility and generate preliminary diagnostic-accuracy estimates for future confirmatory research.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Intraductal Papillary Mucinous Neoplasm of Pancreas | Pancreatic Intraductal Neoplasm | PROBABILISTIC | 0.70 |
| Pancreatic Neoplasms | Pancreatic Neoplasm | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AIWorks-Cholangioscopy Real-Time Artificial Intelligence Model | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Digital POPS
- description
- Adults undergoing clinically indicated digital per-oral pancreatoscopy who receive both the prespecified endoscopist visual assessment and real-time AI assessment during the same procedure.
- interventionNames
- Diagnostic Test: AIWorks-Cholangioscopy Real-Time Artificial Intelligence Model
Primary outcomes (1)
- measure
- Patient-level diagnostic accuracy of real-time artificial intelligence for high-grade dysplasia or invasive carcinoma
- timeFrame
- From baseline to 6 months
- description
- Diagnostic accuracy of the real-time AI model for identifying high-grade dysplasia or invasive carcinoma at the patient level during digital per-oral pancreatoscopy. AI findings will be classified as positive or negative according to the prespecified diagnostic threshold and compared with the reference standard. The primary analysis will report sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy, each with corresponding 95% confidence intervals.
Secondary outcomes (7)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Adults aged 18 years or older. 2. Patients with a clinical indication for digital per-oral pancreatoscopy (POPS) as part of their diagnostic evaluation or preoperative assessment. 3. Patients with one or more of the following clinical indications: 3.1. Suspected or known main-duct or mixed-type IPMN. 3.2. Branch-duct IPMN with suspected communication with the main pancreatic duct and worrisome features or high-risk stigmata. 3.3. Indeterminate main pancreatic duct stricture, filling defect, or intraductal abnormality after cross-sectional imaging and/or EUS. 3.4. Need for preoperative assessment or mapping of IPMN extent. 4. Ability to undergo digital POPS according to the treating team's clinical assessment. 5. Ability to provide written informed consent. 6. Availability of an adequate reference-standard assessment, including histopathology when surgery is performed or tissue/cytologic assessment with clinical and imaging follow-up when surgery is not performed. 7. Willingness and ability to complete the required 6-month clinical/imaging follow-up when a surgical reference standard is not available. Exclusion Criteria: 1. Acute pancreatitis within 2 weeks before the planned pancreatoscopy. 2. Hemodynamic instability or clinical condition precluding safe pancreatoscopy. 3. ASA physical status IV or V when the treating team determines that the procedure cannot be safely performed. 4. Uncorrectable coagulopathy or other contraindication to pancreatoscopy and/or tissue sampling. 5. Pancreatic or gastrointestinal altered anatomy that prevents technically feasible digital POPS. 6. Evidence of unsafe pancreatic duct disruption or another anatomical or procedural condition that makes pancreatoscopy inappropriate. 7. Pregnancy. 8. Inability or unwillingness to provide informed consent. 9. Inability or unwillingness to complete the required follow-up when a non-surgical reference standard is necessary.
References
Publications (5)
- BACKGROUNDBossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, de Vet HC, Kressel HY, Rifai N, Golub RM, Altman DG, Hooft L, Korevaar DA, Cohen JF; STARD Group. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015 Oct 28;351:h5527. doi: 10.1136/bmj.h5527. PMID 26511519
- BACKGROUNDde Jong DM, Stassen PMC, Groot Koerkamp B, Ellrichmann M, Karagyozov PI, Anderloni A, Kylanpaa L, Webster GJM, van Driel LMJW, Bruno MJ, de Jonge PJF; European Cholangioscopy study group. The role of pancreatoscopy in the diagnostic work-up of intraductal papillary mucinous neoplasms: a systematic review and meta-analysis. Endoscopy. 2023 Jan;55(1):25-35. doi: 10.1055/a-1869-0180. Epub 2022 Jun 3. PMID 35668651
- BACKGROUNDKahaleh M, Gaidhane M, Shahid HM, Tyberg A, Sarkar A, Ardengh JC, Kedia P, Andalib I, Gress F, Sethi A, Gan SI, Suresh S, Makar M, Bareket R, Slivka A, Widmer JL, Jamidar PA, Alkhiari R, Oleas R, Kim D, Robles-Medranda CA, Raijman I. Digital single-operator cholangioscopy interobserver study using a new classification: the Mendoza Classification (with video). Gastrointest Endosc. 2022 Feb;95(2):319-326. doi: 10.1016/j.gie.2021.08.015. Epub 2021 Aug 31. PMID 34478737
- BACKGROUNDRobles-Medranda C, Valero M, Soria-Alcivar M, Puga-Tejada M, Oleas R, Ospina-Arboleda J, Alvarado-Escobar H, Baquerizo-Burgos J, Robles-Jara C, Pitanga-Lukashok H. Reliability and accuracy of a novel classification system using peroral cholangioscopy for the diagnosis of bile duct lesions. Endoscopy. 2018 Nov;50(11):1059-1070. doi: 10.1055/a-0607-2534. Epub 2018 Jun 28. PMID 29954008
- BACKGROUNDRobles-Medranda C, Baquerizo-Burgos J, Alcivar-Vasquez J, Kahaleh M, Raijman I, Kunda R, Puga-Tejada M, Egas-Izquierdo M, Arevalo-Mora M, Mendez JC, Tyberg A, Sarkar A, Shahid H, Del Valle-Zavala R, Rodriguez J, Merfea RC, Barreto-Perez J, Saldana-Pazmino G, Calle-Loffredo D, Alvarado H, Lukashok HP. Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model. Endoscopy. 2023 Aug;55(8):719-727. doi: 10.1055/a-2034-3803. Epub 2023 Feb 13.