Intraductal Papillary Mucinous Neoplasm of PancreasNCT07809841
Real-Time AI During Pancreatoscopy for Detection of Pancreatic Neoplasia
An observational study of Intraductal Papillary Mucinous Neoplasm of Pancreas and Pancreatic Neoplasms, run by Instituto Ecuatoriano de Enfermedades Digestivas at 1 site in Ecuador.
Trial summary
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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Intraductal Papillary Mucinous Neoplasm of Pancreas, Pancreatic Neoplasms
- What is being tested?
- AIWorks-Cholangioscopy Real-Time Artificial Intelligence Model
- Type of treatment
- Pill or oral medicine
- How many people
- 60 participants
- Where
- 1 siteEcuador
- Study dates
- 15 Sep 2026to 30 Aug 2027
- How long will I take part?
- 6 Months
- Phase
- Observational
- 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
Study population: The study population will consist of adults aged 18 years or older undergoing clinically indicated digital per-oral pancreatoscopy (POPS) at the Instituto Ecuatoriano de Enfermedades Digestivas (IECED). Participants will be patients evaluated for suspected or known intraductal papillary mucinous neoplasm (IPMN), indeterminate pancreatic duct abnormalities, including strictures or filling defects, or for preoperative assessment and mapping of IPMN extent. Approximately 60 evaluable participants will be included. All participants will undergo the same prospective diagnostic assessment sequence, consisting of an initial endoscopist visual assessment followed by real-time application of the AIWorks-Cholangioscopy artificial intelligence model during the same pancreatoscopy examination. Participants will subsequently be evaluated against the prespecified reference standard based on surgical histopathology when available or tissue sampling and clinical/imaging follow-up.
Who may be able to join
- Adults aged 18 years or older.
- Patients with a clinical indication for digital per-oral pancreatoscopy (POPS) as part of their diagnostic evaluation or preoperative assessment.
- 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.
Show all 10Show fewer
- Ability to undergo digital POPS according to the treating team's clinical assessment.
- Ability to provide written informed consent.
- 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.
- Willingness and ability to complete the required 6-month clinical/imaging follow-up when a surgical reference standard is not available.
Who cannot join
- Acute pancreatitis within 2 weeks before the planned pancreatoscopy.
- Hemodynamic instability or clinical condition precluding safe pancreatoscopy.
- ASA physical status IV or V when the treating team determines that the procedure cannot be safely performed.
- Uncorrectable coagulopathy or other contraindication to pancreatoscopy and/or tissue sampling.
- Pancreatic or gastrointestinal altered anatomy that prevents technically feasible digital POPS.
- Evidence of unsafe pancreatic duct disruption or another anatomical or procedural condition that makes pancreatoscopy inappropriate.
Show all 9Show fewer
- Pregnancy.
- Inability or unwillingness to provide informed consent.
- Inability or unwillingness to complete the required follow-up when a non-surgical reference standard is necessary.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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
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.
Read the detailed description
This prospective pilot diagnostic-accuracy study will evaluate the performance of a previously validated artificial intelligence (AI) model when applied in real time during digital per-oral pancreatoscopy (POPS) for the identification of pancreatic neoplastic lesions and intraductal papillary mucinous neoplasm (IPMN).
Adults undergoing clinically indicated digital POPS at the Instituto Ecuatoriano de Enfermedades Digestivas (IECED) will be prospectively enrolled. Eligible patients will include those undergoing pancreatoscopy for suspected or known main-duct or mixed-type IPMN, branch-duct IPMN with suspected main-duct communication and concerning features, indeterminate pancreatic-duct strictures or filling defects, or preoperative assessment and mapping of IPMN extent.
During each POPS examination, the endoscopist will first perform and record a conventional visual assessment while the AI system remains hidden. The AI system, AIWorks-Cholangioscopy, will subsequently be activated during the same examination. The previously validated model was developed and validated using digital cholangioscopy data and will be applied to digital pancreatoscopy video without modification of its model weights. AI-generated findings will be recorded independently and compared with the endoscopist's initial assessment.
The AI system will provide real-time visual information, including detection and localization of suspected abnormal areas. AI findings will be documented as an index diagnostic test and will not independently determine patient management. Tissue sampling and subsequent clinical management will remain at the discretion of the treating endoscopist and multidisciplinary team according to standard clinical practice. When feasible, findings identified by either the endoscopist or AI may be documented for correlation with subsequent tissue sampling. The study will also record whether AI findings were concordant or discordant with the initial endoscopist assessment and whether the information was considered during the procedure.
The reference standard will consist of surgical histopathology when pancreatic resection is performed. In patients who do not undergo surgery, the reference assessment will be based on available intraductal tissue sampling and/or cytology together with clinical, imaging, and endoscopic follow-up for up to 6 months. Histopathologic assessment will be performed independently of the AI findings and the endoscopist's locked pre-AI assessment whenever feasible.
The primary diagnostic endpoint will be patient-level identification of high-grade dysplasia or invasive carcinoma, classified as a binary outcome of high-grade dysplasia/invasive carcinoma versus all other diagnostic categories. Diagnostic performance of real-time AI will be estimated using sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy, with corresponding 95% confidence intervals.
Secondary analyses will evaluate the diagnostic performance of the endoscopist's initial visual assessment, agreement and discordance between AI and endoscopist assessments, identification of IPMN epithelium, segment-level findings, technical feasibility of real-time AI application, and the relationship between AI findings and subsequent tissue sampling or clinical decision-making. Procedural safety will also be assessed through recording of adverse events occurring within 30 days of pancreatoscopy.
The study is designed as a pilot investigation. The planned evaluable sample is 60 participants, with up to approximately 70 participants potentially screened or enrolled to account for exclusions and non-evaluable examinations. The pilot is intended to generate preliminary patient-level diagnostic-accuracy estimates, evaluate the feasibility of real-time AI application during digital POPS, characterize AI-endoscopist concordance, and provide parameters for the design and sample-size planning of a future confirmatory diagnostic-accuracy study.
- Being tested
- AIWorks-Cholangioscopy Real-Time Artificial Intelligence Model
- Medical subject headings
- Pancreatic Intraductal Neoplasms, Pancreatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIWorks-Cholangioscopy Real-Time Artificial Intelligence ModelDiagnostic test
A previously validated artificial intelligence model developed for digital cholangioscopy will be applied in real time to digital per-oral pancreatoscopy video without modification of its model weights. The system provides real-time visual detection and localization of suspected pancreatic duct abnormalities during pancreatoscopy. The AI assessment will be performed after the endoscopist has completed and locked the initial visual assessment. AI findings will be recorded as an index diagnostic test and will not independently determine tissue sampling, treatment, surgery, or other clinical management.
| Study group | Role | What they receive |
|---|---|---|
| Digital POPS | 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. Receives: Diagnostic Test: AIWorks-Cholangioscopy Real-Time Artificial Intelligence Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient-level diagnostic accuracy of real-time artificial intelligence for high-grade dysplasia or invasive carcinoma
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.
Measured: From baseline to 6 months
Secondary outcomes (7)
Endoscopist diagnostic accuracy for high-grade dysplasia or invasive carcinoma
Diagnostic performance of the endoscopist's initial visual assessment, performed before activation of the AI overlay, for identification of high-grade dysplasia or invasive carcinoma at the patient level. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy will be calculated using the prespecified reference standard.
Measured: From baseline to 6 months
Agreement between artificial intelligence and endoscopist assessments
Concordance and discordance between the AI assessment and the endoscopist's pre-AI visual assessment for identification of suspected neoplastic lesions and high-grade dysplasia or invasive carcinoma. Agreement will be summarized using paired proportions and, when appropriate, Cohen's kappa coefficient.
Measured: During Index procedure
Diagnostic accuracy for IPMN epithelium
Diagnostic performance of the real-time AI model for identification of IPMN epithelium, classified as IPMN epithelium present versus absent, using the prespecified reference standard.
Measured: From baseline to 6 months follow up
Segment-level detection of abnormal pancreatic duct areas
Ability of the AI model to identify and localize abnormal pancreatic duct segments during digital per-oral pancreatoscopy. AI-positive segments will be compared with corresponding endoscopist assessments and available tissue or cytologic findings.
Measured: During index procedure
AI-endoscopist discordance and additional diagnostic information
Frequency and characteristics of cases in which the AI assessment differs from the initial endoscopist assessment, including AI-positive/endoscopist-negative and AI-negative/endoscopist-positive findings. The analysis will describe whether discordant AI findings were subsequently correlated with tissue sampling or other reference-standard findings.
Measured: From baseline to 6 months follow up
Change in intended clinical management after AI assessment
Frequency with which the AI findings are associated with a documented change in the endoscopist's intended sampling strategy or intended surgical assessment after review of the real-time AI findings. Any change will be recorded descriptively and will not be mandated by the study protocol.
Measured: During Index Procedure
Procedure-related adverse events
Incidence and severity of adverse events occurring within 30 days after digital per-oral pancreatoscopy, classified according to the American Society for Gastrointestinal Endoscopy (ASGE) lexicon.
Measured: From index procedure through 30 days after the procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Sep 2026
- Study start15 Sep 2026 (estimated)
- Primary completion30 May 2027 (estimated)
- Study completion30 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Intraductal Papillary Mucinous Neoplasm of PancreasStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 60 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades Digestivas
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07809841
- Sponsor's protocol ID
- IECED-AIPOPS-0902026
- Status last verified
- Sep 2026
- First submitted
- 3 Sep 2026
- Last updated
- 9 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared with other researchers. The study is a small, single-center pilot diagnostic-accuracy study, and sharing individual-level data could present a risk of participant re-identification given the limited sample size and the specific clinical and procedural characteristics of the study population. Aggregate study results will be reported in accordance with applicable ethical, regulatory, and institutional requirements.
Publications
Bossuyt 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.
PubMed 26511519de 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.
PubMed 35668651Kahaleh 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.
PubMed 34478737Robles-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.
PubMed 29954008Robles-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.
PubMed 36781156
Study at a glance
From the registry record, last updated by the sponsor on 9 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07809841
- Acronym / sponsor ID
- IECED-AIPOPS-0902026
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades DigestivasUniversity, hospital or other
- Study start
- 15 Sep 2026 (estimated)
- Primary completion
- 30 May 2027 (estimated)
- Study completion
- 30 Aug 2027 (estimated)
- First posted
- 9 Sep 2026 (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 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.
Detailed description
This prospective pilot diagnostic-accuracy study will evaluate the performance of a previously validated artificial intelligence (AI) model when applied in real time during digital per-oral pancreatoscopy (POPS) for the identification of pancreatic neoplastic lesions and intraductal papillary mucinous neoplasm (IPMN).
Adults undergoing clinically indicated digital POPS at the Instituto Ecuatoriano de Enfermedades Digestivas (IECED) will be prospectively enrolled. Eligible patients will include those undergoing pancreatoscopy for suspected or known main-duct or mixed-type IPMN, branch-duct IPMN with suspected main-duct communication and concerning features, indeterminate pancreatic-duct strictures or filling defects, or preoperative assessment and mapping of IPMN extent.
During each POPS examination, the endoscopist will first perform and record a conventional visual assessment while the AI system remains hidden. The AI system, AIWorks-Cholangioscopy, will subsequently be activated during the same examination. The previously validated model was developed and validated using digital cholangioscopy data and will be applied to digital pancreatoscopy video without modification of its model weights. AI-generated findings will be recorded independently and compared with the endoscopist's initial assessment.
The AI system will provide real-time visual information, including detection and localization of suspected abnormal areas. AI findings will be documented as an index diagnostic test and will not independently determine patient management. Tissue sampling and subsequent clinical management will remain at the discretion of the treating endoscopist and multidisciplinary team according to standard clinical practice. When feasible, findings identified by either the endoscopist or AI may be documented for correlation with subsequent tissue sampling. The study will also record whether AI findings were concordant or discordant with the initial endoscopist assessment and whether the information was considered during the procedure.
The reference standard will consist of surgical histopathology when pancreatic resection is performed. In patients who do not undergo surgery, the reference assessment will be based on available intraductal tissue sampling and/or cytology together with clinical, imaging, and endoscopic follow-up for up to 6 months. Histopathologic assessment will be performed independently of the AI findings and the endoscopist's locked pre-AI assessment whenever feasible.
The primary diagnostic endpoint will be patient-level identification of high-grade dysplasia or invasive carcinoma, classified as a binary outcome of high-grade dysplasia/invasive carcinoma versus all other diagnostic categories. Diagnostic performance of real-time AI will be estimated using sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy, with corresponding 95% confidence intervals.
Secondary analyses will evaluate the diagnostic performance of the endoscopist's initial visual assessment, agreement and discordance between AI and endoscopist assessments, identification of IPMN epithelium, segment-level findings, technical feasibility of real-time AI application, and the relationship between AI findings and subsequent tissue sampling or clinical decision-making. Procedural safety will also be assessed through recording of adverse events occurring within 30 days of pancreatoscopy.
The study is designed as a pilot investigation. The planned evaluable sample is 60 participants, with up to approximately 70 participants potentially screened or enrolled to account for exclusions and non-evaluable examinations. The pilot is intended to generate preliminary patient-level diagnostic-accuracy estimates, evaluate the feasibility of real-time AI application during digital POPS, characterize AI-endoscopist concordance, and provide parameters for the design and sample-size planning of a future confirmatory diagnostic-accuracy study.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Digital POPS | – | Diagnostic Test: AIWorks-Cholangioscopy Real-Time Artificial Intelligence ModelAdults undergoing clinically indicated digital per-oral pancreatoscopy who receive both the prespecified endoscopist visual assessment and real-time AI assessment during the same procedure. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of adults aged 18 years or older undergoing clinically indicated digital per-oral pancreatoscopy (POPS) at the Instituto Ecuatoriano de Enfermedades Digestivas (IECED). Participants will be patients evaluated for suspected or known intraductal papillary mucinous neoplasm (IPMN), indeterminate pancreatic duct abnormalities, including strictures or filling defects, or for preoperative assessment and mapping of IPMN extent. Approximately 60 evaluable participants will be included. All participants will undergo the same prospective diagnostic assessment sequence, consisting of an initial endoscopist visual assessment followed by real-time application of the AIWorks-Cholangioscopy artificial intelligence model during the same pancreatoscopy examination. Participants will subsequently be evaluated against the prespecified reference standard based on surgical histopathology when available or tissue sampling and clinical/imaging follow-up.
Key inclusion criteria (10 listed)
- Adults aged 18 years or older.
- Patients with a clinical indication for digital per-oral pancreatoscopy (POPS) as part of their diagnostic evaluation or preoperative assessment.
- 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.
Key exclusion criteria (9 listed)
- Acute pancreatitis within 2 weeks before the planned pancreatoscopy.
- Hemodynamic instability or clinical condition precluding safe pancreatoscopy.
- ASA physical status IV or V when the treating team determines that the procedure cannot be safely performed.
- Uncorrectable coagulopathy or other contraindication to pancreatoscopy and/or tissue sampling.
- Pancreatic or gastrointestinal altered anatomy that prevents technically feasible digital POPS.
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 |
|---|---|---|
| AIWorks-Cholangioscopy Real-Time Artificial Intelligence ModelA previously validated artificial intelligence model developed for digital cholangioscopy will be applied in real time to digital per-oral pancreatoscopy video without modification of its model weights. The system provides real-time visual detection and localization of suspected pancreatic duct abnormalities during pancreatoscopy. The AI assessment will be performed after the endoscopist has completed and locked the initial visual assessment. AI findings will be recorded as an index diagnostic test and will not independently determine tissue sampling, treatment, surgery, or other clinical management. | Diagnostic test | Digital POPS |
Primary outcome
Patient-level diagnostic accuracy of real-time artificial intelligence for high-grade dysplasia or invasive carcinoma
Time frame: From baseline to 6 months
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.
7 secondary outcomes
Endoscopist diagnostic accuracy for high-grade dysplasia or invasive carcinoma
Time frame: From baseline to 6 months
Agreement between artificial intelligence and endoscopist assessments
Time frame: During Index procedure
Diagnostic accuracy for IPMN epithelium
Time frame: From baseline to 6 months follow up
Segment-level detection of abnormal pancreatic duct areas
Time frame: During index procedure
AI-endoscopist discordance and additional diagnostic information
Time frame: From baseline to 6 months follow up
Change in intended clinical management after AI assessment
Time frame: During Index Procedure
Procedure-related adverse events
Time frame: From index procedure through 30 days after the procedure
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 9 Sep 2026.
- Background: PubMed 26511519Bossuyt 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.
- Background: PubMed 35668651de 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.
- Background: PubMed 34478737Kahaleh 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.
- Background: PubMed 29954008Robles-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.
- Background: PubMed 36781156Robles-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.
- Individual participant dataSharing planned: No. Individual participant data will not be shared with other researchers. The study is a small, single-center pilot diagnostic-accuracy study, and sharing individual-level data could present a risk of participant re-identification given the limited sample size and the specific clinical and procedural characteristics of the study population. Aggregate study results will be reported in accordance with applicable ethical, regulatory, and institutional requirements.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Intraductal Papillary Mucinous Neoplasm of Pancreas and Pancreatic Neoplasms, run by Instituto Ecuatoriano de Enfermedades Digestivas at 1 site in Ecuador.
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 10 inclusion and 9 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: opening soon, as of the sponsor's last update on 9 Sep 2026. 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 Ecuador. 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 Carlos Robles-Medranda, MD, FASGE, AGAF (042109180, carlosoakm@yahoo.es), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient-level diagnostic accuracy of real-time artificial intelligence for high-grade dysplasia or invasive carcinoma (time frame: From baseline to 6 months). 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 NCT07809841, which also lists 5 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 9 Sep 2026; AzTrials last refreshed its copy on 8 Nov 2026.