Trial · NCT07900776
AI-Assisted Differentiation of Autoimmune Pancreatitis and Pancreatic Cancer Using Endoscopic Ultrasound
NCT07900776, Not yet recruiting, Ruijin Hospital
3 sourcestrials-2026-11-12-0.5.0
Cite this record
ConductScience Library. AI-Assisted Differentiation of Autoimmune Pancreatitis and Pancreatic Cancer Using Endoscopic Ultrasound. Trials, record NCT07900776. Release trials-2026-11-12-0.5.0, 12 Nov 2026. https://conductscience.org/library/trials/NCT07900776
On this page
Trial
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
- Title
- AI-Assisted Differentiation of Autoimmune Pancreatitis and Pancreatic Cancer Using Endoscopic Ultrasound
- NCT number
- NCT07900776
- Status
- Not yet recruiting
- Enrollment
- 690 participants, estimated
- Lead sponsor
- Ruijin Hospital
- Conditions
- Autoimmune Pancreatitis, Pancreatic Ductal Adenocarcinoma (PDAC)
- First posted
- 2026-10-28
- Last updated
- 2026-11-09
Source ClinicalTrials.gov
Registered outcomes10
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description |
|---|---|---|---|
| Primary | Area Under the Receiver Operating Characteristic Curve (AUC) of the AI Model for Differentiating f-AIP From PDAC | At completion of data collection and diagnostic assessment in the prospective validation cohort, through December 2028. | The diagnostic performance of the multimodal artificial intelligence (AI) model in differentiating focal autoimmune pancreatitis (f-AIP) from pancreatic ductal adenocarcinoma (PDAC) will be evaluated using the area under the receiver operating characteristic curve (AUC) in an independent prospective multicenter validation cohort. The AUC ranges from 0.5 (no discrimination) to 1.0 (perfect discrimination), with higher values indicating better diagnostic performance. The AUC and its 95% confidence interval will be calculated. |
| Secondary | Sensitivity of the AI Model for Differentiating f-AIP From PDAC | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | Sensitivity will be calculated as the proportion of patients with focal autoimmune pancreatitis (f-AIP) correctly identified by the AI model among all patients with confirmed f-AIP in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals. |
| Secondary | Specificity of the AI Model for Differentiating f-AIP From PDAC | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | Specificity will be calculated as the proportion of patients with pancreatic ductal adenocarcinoma (PDAC) correctly identified as not having f-AIP among all patients with confirmed PDAC in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals. |
| Secondary | Diagnostic Accuracy of the AI Model | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | Diagnostic accuracy will be defined as the proportion of correctly classified f-AIP and PDAC cases among all evaluated patients in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals. |
| Secondary | Positive Predictive Value of the AI Model | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | Positive predictive value (PPV) will be calculated as the proportion of patients with confirmed f-AIP among all patients classified as f-AIP by the AI model in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals. |
| Secondary | Negative Predictive Value of the AI Model | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | Negative predictive value (NPV) will be calculated as the proportion of patients with confirmed PDAC among all patients classified as PDAC by the AI model in the prospective validation cohort. Results will be reported as percentages with 95% confidence intervals. |
| Secondary | Comparison of Diagnostic Performance Between the AI Model and Endoscopists | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | The diagnostic performance of the AI model will be compared with that of endoscopists at different experience levels (junior: less than 5 years; intermediate: 5-10 years; senior: 10 years or more). Diagnostic performance will be assessed using AUC, sensitivity, specificity, and accuracy in the prospective validation cohort. Differences in AUC will be evaluated using the DeLong test where applicable. |
| Secondary | Comparison of Diagnostic Sensitivity Between the AI Model and Endoscopists | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | The sensitivity of the AI model for differentiating mass-forming autoimmune pancreatitis from pancreatic ductal adenocarcinoma will be compared with that of endoscopists at different experience levels in the prospective validation cohort. Sensitivity will be calculated as the proportion of patients with pancreatic ductal adenocarcinoma correctly identified as having pancreatic ductal adenocarcinoma. |
| Secondary | Comparison of Diagnostic Specificity Between the AI Model and Endoscopists | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | The specificity of the AI model for differentiating mass-forming autoimmune pancreatitis from pancreatic ductal adenocarcinoma will be compared with that of endoscopists at different experience levels in the prospective validation cohort. Specificity will be calculated as the proportion of patients with mass-forming autoimmune pancreatitis correctly identified as having mass-forming autoimmune pancreatitis. |
| Secondary | Comparison of Diagnostic Accuracy Between the AI Model and Endoscopists | At completion of diagnostic assessment in the prospective validation cohort, through December 2028. | The diagnostic accuracy of the AI model will be compared with that of endoscopists at different experience levels in the prospective validation cohort. Accuracy will be calculated as the proportion of correctly classified patients among all evaluated patients. |
Source ClinicalTrials.gov
Outcomes0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
None in this release.
Results by group0
Each measurement the trial posted, group by group, in the words and numbers it posted.
None in this release.
Participant flow0
How many started, completed or left each group, period by period.
None in this release.
Adverse events0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
None in this release.
Adverse event totals0
The totals the trial posted for each group.
None in this release.
Papers0
The papers the trial lists on ClinicalTrials.gov.
None in this release.
Grants0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
None in this release.
Trial comparisons0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
None in this release.
Tests0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
None in this release.
Data dates
The day each source was copied for this release.
Sources 3
- ClinicalTrials.gov's posted results, through CTTI's AACTFreely available. Acknowledgement requested
- ClinicalTrials.govNo license stated
- NIH RePORTER's clinical studies and project filesPublic domain
Each source's terms
- ClinicalTrials.gov's posted results, through CTTI's AACTClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299
- ClinicalTrials.govnone stated
- NIH RePORTER's clinical studies and project filesPublic domain (NIH RePORTER)
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
For software
Read this trial from software.
The record and each of its lists are JSON, twenty rows a page. No key.
GET https://conductscience.org/api/trials/v1/NCT07900776
GET https://conductscience.org/api/trials/v1/NCT07900776/registered