Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens
Status: Recruiting
Location: See location...
Intervention Type: Device
Study Type: Interventional
Study Phase: Not Applicable
SUMMARY
The goal of this study is to investigate the ability of a machine learning model to evaluate two-photon fluorescence microscopy images of dermatologic biopsies at point of care. The main question it aims to answer is: • How well do two-photon fluorescence images of biopsies taken in a clinic and evaluated by a machine learning model agree with conventional histology?
Eligibility
Participation Requirements
Sex: All
Healthy Volunteers: f
View:
• Punch, excisional or shave biopsy specimen
Locations
United States
New York
Rochester Dermatologic Surgery
RECRUITING
Victor
Contact Information
Primary
Michael Giacomelli, Ph.D
mgiacome@ur.rochester.edu
5852766260
Time Frame
Start Date: 2026-06-24
Estimated Completion Date: 2027-07-01
Participants
Target number of participants: 92
Treatments
Experimental: TPFM imaging of biopsy
Specimens will be imaged with TPFM and diagnosed using a machine learning model
Related Therapeutic Areas
Sponsors
Collaborators: National Cancer Institute (NCI), Rochester Dermatologic Surgery
Leads: University of Rochester