Artificial Intelligence (AI) Cytopathology Trial

Purpose

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.

Condition

  • Pancreatic Solid Lesions

Eligibility

Eligible Ages
Between 18 Years and 100 Years
Eligible Genders
All
Accepts 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.

Study Design

Phase
Study Type
Observational
Observational Model
Case-Only
Time Perspective
Prospective

Arm Groups

ArmDescriptionAssigned Intervention
Prospective enrollment All subjects will be enrolled prospectively. Subjects will be included in the study after eligibility is assessed and informed consent is obtained. The slide scanner will scan the slides on site and the images will be securely saved and sent for interpretation by the AI software at a different location. The results of the AI interpretation of the slides will be blinded to the on-site procedure team including the endoscopist and cytopathologist until the final pathology report is complete.
  • 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 proprietary system to analyze slides from EUS FNA/FNB samples at bedside.

Recruiting Locations

Memorial Hermann Hospital
Houston, Texas 77030
Contact:
Prithvi B Patil
713-480-1179
prithvi.b.patil@uth.tmc.edu

More Details

Status
Recruiting
Sponsor
The University of Texas Health Science Center, Houston

Study Contact

Prithvi B Patil, MS
7135006456
prithvi.b.patil@uth.tmc.edu