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Artificial Intelligence (AI) Cytopathology Trial

Artificial Intelligence for Rapid On-site Evaluation (AI-ROSE) for Endoscopic Ultrasound-guided Fine-needle Aspiration (EUS-FNA) Biopsy of Pancreatic Solid Lesions: A Prospective Double Blinded Study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05018663
Enrollment
50
Registered
2021-08-24
Start date
2021-07-21
Completion date
2028-01-30
Last updated
2023-02-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pancreatic Solid Lesions

Keywords

Endoscopic Ultrasound guided fine needle aspiration (FNA), Endoscopic Ultrasound guided fine needle biopsy (FNB)

Brief summary

Purpose The primary objective of the study is to compare interpretation of EUS FNA/FNB samples for adequacy between ROSE and AI at bedside. To compare accuracy of preliminary diagnosis results between ROSE and AI at bedside versus final pathology report. Research design This is a prospective single center study to compare performance characteristics in the interpretation of EUS FNA/FNB samples between AI and ROSE. Procedures to be used Eligible patients will undergo EUS guided FNA/FNA of PSLs using standard of care. Sample slides are prepared by a cytopathologist at bedside and observed under a microscope. At the same time, the slides are scanned using a slide scanner and those images are saved for interpretation by AI at a later time.

Interventions

OTHERArtificial Intelligence software ROSE

Rapid on-site evaluation (ROSE) of Endoscopic Ultrasound (EUS) guided FNA/FNB (Fine Needle Aspirate/Fine Needle Biopsy) of pancreatic solid lesions (PSLs) has been shown in improve diagnostic yield. The availability and performance of ROSE at EUS performing centers is variable. With strides in Artificial Intelligence (AI) capabilities over the years, the University of Texas at Health Sciences Center at Houston in collaboration with Haystac is developing an artificial intelligence based proprietary system to analyze slides from EUS FNA/FNB samples at bedside.

Sponsors

The University of Texas Health Science Center, Houston
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No

Inclusion criteria

* Have EUS finding of a PSL; * Do not have contraindications for FNA/FNB.

Exclusion criteria

* Inability to provide informed consent for the procedure; * Contraindication for FNA/FNB eg coagulopathy, lack of avascular window for FNA.

Design outcomes

Primary

MeasureTime frameDescription
Detection the adequacy for diagnosisDuring procedureThe primary outcome of the study is to determine how AI compares with ROSE in determining if EUS FNA/FNB sample from PSLs is adequate for diagnosis. This will be interpreted as a percentage in each group. The main study parameter is on-site determination if an EUS FNA/FNB sample is adequate for interpretation and diagnosis

Secondary

MeasureTime frameDescription
Comparing the accuracy between preliminary diagnosisDuring procedureTo compare the accuracy between AI and ROSE preliminary diagnosis versus the final pathology report. Interpretation of preliminary results will be divided into categories of benign vs malignancy, acinar cells vs ductal cells in benign, adenocarcinoma vs neuroendocrine tumor vs other in malignancy.

Countries

United States

Contacts

Primary ContactPrithvi B Patil, MS
prithvi.b.patil@uth.tmc.edu7135006456

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026