Artificial Intelligence (AI) Cytopathology Trial
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Artificial Intelligence software ROSE.
- Who it may be relevant to
- Registry conditions: Pancreatic Solid Lesions. Basic parameters: 18 years — 100 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
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
Overview
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
- Other Artificial 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 proprieta
Primary outcome measures
- Detection the adequacy for diagnosis [Time frame: During procedure]
Secondary outcome measures (1)
- Comparing the accuracy between preliminary diagnosis [Time frame: During procedure]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-only
Study locations
United States · 1 center
- Memorial Hermann Hospital — Houston
Identifiers
NCT: NCT05018663 · HSC-MS-21-0051