Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
AgeNot listed
SponsorUniversity of Rochester
About this trial
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 criteria
Qualifiers
Punch, excisional or shave biopsy specimen
Disqualifiers
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
Trial design
Treatments tested in this trial
- Two photon microscopy imaging
Treatment groups
92 Participants
are divided into 1 treatment groupSponsors and collaborators
University of Rochester
Lead sponsor
National Cancer Institute (NCI)
Collaborator
Rochester Dermatologic Surgery
Collaborator
Source ClinicalTrials.gov