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

NCT07682831 | Not Yet Recruiting FDA Device

• 2 other identifiers: STUDY00009823BR37CA258376
• Study Type: interventional
• Target: 92
• Locations: 1 country
• Sites: 1

Brief 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)

Keywords

two-photon microscopy; machine learning

Outcome Measures

Primary Outcomes (2)

• Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care

  A machine learning model will evaluate TPFM images of patient biopsies at point of care. Sensitivity will be calculated for the machine learning model using two photon fluorescence microscopy images. Sensitivity is defined as the number of true positive diagnoses divided by the sum of true positive and false negative diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.

  During or immediately following patient biopsy (same day)

• Specificity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care

  A machine learning model will evaluate TPFM images of patient biopsies at point of care. Specificity will be calculated for the machine learning model using two photon fluorescence microscopy images. Specificity is defined as the number of true negative diagnoses divided by the sum of true negative and false positive diagnoses among biopsy specimens for which the machine learning model provides a definitive diagnosis. The patient's ultimate clinical diagnosis will serve as the reference standard.

  During or immediately following patient biopsy (same day)

Secondary Outcomes (2)

• Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors

  After completion of patient diagnosis (typically 1-2 weeks after procedure)

• Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis

  During or immediately following patient biopsy (same day)

Study Arms (1)

TPFM imaging of biopsy

EXPERIMENTAL

Specimens will be imaged with TPFM and diagnosed using a machine learning model

Device: Two photon microscopy imaging

Interventions

Two photon microscopy imaging DEVICE

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

TPFM imaging of biopsy

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

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

Sponsors & Collaborators

• University of Rochester: lead
• National Cancer Institute (NCI): collaborator
• Rochester Dermatologic Surgery: collaborator

Study Sites (1)

Rochester Dermatologic Surgery

Victor, New York, 14654, United States

Location

MeSH Terms

Conditions

Carcinoma, Basal Cell; Carcinoma, Squamous Cell

Condition Hierarchy (Ancestors)

Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Basal Cell; Neoplasms, Squamous Cell

Central Study Contacts

Michael Giacomelli, Ph.D

CONTACT

5852766260; mgiacome@ur.rochester.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: June 25, 2026
• First Posted: July 6, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): July 1, 2027
• Last Updated: July 6, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL

Locations

US (1)