RECRUITING Not applicable ClinicalTrials.gov

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

Sponsor: University of Rochester • ClinicalTrials.gov ID: NCT07682831

Registry updated: 2026-08-19

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Description

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?

Interventions

Two photon microscopy imaging (Device)
Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis

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

Conditions

Basal Cell Carcinoma of Skin Squamous Cell Carcinoma (Skin)

Locations

Rochester Dermatologic Surgery — Victor, New York, United States

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Official Source

Data sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.

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