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Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies

RECRUITINGN/ASponsored by University of Rochester
Actively Recruiting
PhaseN/A
SponsorUniversity of Rochester
Started2026-06-24
Est. completion2027-06-01
Eligibility
Healthy vol.Accepted
Locations1 site
View on ClinicalTrials.gov →

NCT07682831

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

Healthy volunteers accepted
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

Conditions3

Basal Cell Carcinoma of SkinCancerSquamous Cell Carcinoma (Skin)

Locations1 site

Rochester Dermatologic Surgery
Victor, New York, 14654
Sherrif Ibrahim, M.D.-Ph.D.585-222-1400dr.ibrahim@rochesterdermsurgery.com

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