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

RecruitingNA

Sponsor: University of Rochester

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

Interventions: Two photon microscopy imaging

Countries: United States

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 overview

Sex: ALL

Healthy volunteers: No

Study type: INTERVENTIONAL

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

Locations (1)

Victor, New York, United States