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Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens
Who can join
All ages · All sexes
Full eligibility criteria
Inclusion Criteria: * Punch, excisional or shave biopsy specimen Exclusion Criteria: * Biopsy indication includes melanoma or dysplastic/atypical nevus * Excision thickness of less than 1 mm * Excision longest dimension less than 2 mm * Excision performed as multiple pieces in a single specimen container
About the study
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?
What is being tested
- Two photon microscopy imaging (device)
Sponsor: University of Rochester · Participants: 92 · Started: Jun 24, 2026
Contact the study team
- Michael Giacomelli, Ph.D · Phone: 5852766260
Official record on ClinicalTrials.gov — NCT07682831
Locations in the U.S.
| New York | Rochester Dermatologic Surgery, Victor |
Conditions
From ClinicalTrials.gov, data retrieved Oct 2, 2026. Each study sets its own eligibility; the study team decides who can join.