IECED
Guayaquil, Guayas, 090505, Ecuador
NCT Number: NCT07809841
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.
Trial opening soon.
Get Notified18 year and older
All sexes
Observational
Guayaquil, Guayas, 090505, Ecuador
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
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.
Exclusion criteria
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.
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.
Time frame: From baseline to 6 months
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.
Time frame: During Index procedure
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.
Time frame: From baseline to 6 months follow up
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.
Time frame: During index procedure
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.
Time frame: From baseline to 6 months follow up
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.
Time frame: During Index Procedure
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.
Time frame: From index procedure through 30 days after the procedure
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.
Contact information is provided by the study sponsor or research team.
Instituto Ecuatoriano de Enfermedades Digestivas
Other
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
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