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

Starting soon · Not applicable

Conditions studied: Basal Cell Carcinoma of Skin, Squamous Cell Carcinoma (Skin)

In brief

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?

Key facts

Study ID

NCT07682831

Run by

University of Rochester

People needed

92

Starts

2026-06-01

Expected to finish

2027-07-01

Last updated by the study team

2026-07-06

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Punch, excisional or shave biopsy specimen

You may not qualify if…

• 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

Where it is running

• Rochester Dermatologic Surgery — Victor, New York, United States

Full record on ClinicalTrials.gov

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