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

NCT07682831 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 92

Last updated 2026-07-06

No results posted yet for this study

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?

Conditions

  • Basal Cell Carcinoma of Skin
  • Squamous Cell Carcinoma (Skin)

Interventions

DEVICE

Two photon microscopy imaging

Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis

Sponsors & Collaborators

  • National Cancer Institute (NCI)

    collaborator NIH

  • Rochester Dermatologic Surgery

    collaborator OTHER

  • University of Rochester

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-06-01
Primary Completion
2027-06-01
Completion
2027-07-01
FDA Device
Yes

Countries

  • United States

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07682831 on ClinicalTrials.gov