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

Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens

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?

Study Overview

• Status: Not yet recruiting
• Conditions: Basal Cell Carcinoma of Skin; Squamous Cell Carcinoma (Skin)
• Intervention / Treatment: Device: Two photon microscopy imaging

Detailed Description

This study will image biopsy specimens at point of care using two-photon fluorescence microscopy (TPFM) and then assess how well the images predict the eventual clinical diagnosis using a machine learning model. Because two-photon images can be acquired from small biopsy specimens within minutes of excision, they could potentially be used to immediately diagnose patients, but the accuracy of TPFM for various skin conditions is unknown.

Individual biopsy specimens in a dermatology clinic will be imaged using TPFM shortly after biopsy procedures. Immediately following imaging, a machine learning model will evaluate the TPFM images then compute a confidence score for a diagnosis of basal cell carcinoma (BCC), squamous cell carcinoma, and non-cancer. The relative confidence in each diagnosis will be compared, and if sufficient confidence is achieved, the model will render a diagnosis or else flag the specimen as indeterminate for manual pathologist review. This workflow will evaluate the use of ML + TPFM to perform point of care diagnosis of skin lesions.

Following TPFM imaging, the specimen will be submitted for histological processing, which will guide actual patient treatment. Following conclusion of patient treatment, the resulting histology slides will be scanned for comparison and the final patient diagnosis recorded. Images of the histology slides will be read by a pathologist to establish a gold-standard diagnosis. The official diagnosis and the diagnosis from the collaborating pathologist will be compared.

Patient treatment will still be decided by conventional histopathology. TPFM will not be used to change treatment.

• Study Type: Interventional
• Enrollment (Estimated): 92
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Michael Giacomelli, Ph.D; Phone Number: 5852766260; Email: mgiacome@ur.rochester.edu

Study Locations

• United States
  • New York
    • Victor, New York, United States, 14654
      • Rochester Dermatologic Surgery
      • Contact: Sherrif Ibrahim, M.D.-Ph.D.; Phone Number: 585-222-1400; Email: dr.ibrahim@rochesterdermsurgery.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: TPFM imaging of biopsy; Specimens will be imaged with TPFM and diagnosed using a machine learning model	Device: Two photon microscopy imaging; Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion of Discordant Diagnoses Attributable to Machine Learning Model Interpretation Errors	For biopsy specimens with discordant diagnoses between the machine learning model and the patient's ultimate clinical diagnosis, a dermatopathologist will review each case and classify the source of disagreement as machine learning model interpretation error, image quality limitation, or image coregistration error. The proportion of discordant diagnoses attributable to each source of disagreement will be reported.	After completion of patient diagnosis (typically 1-2 weeks after procedure)
Proportion of Biopsy Specimens With a Definitive Machine Learning Diagnosis	The proportion of biopsy specimens for which the machine learning model provides a definitive diagnosis based on two photon fluorescence microscopy images will be calculated as the number of specimens receiving a definitive diagnosis divided by the total number of specimens evaluated.	During or immediately following patient biopsy (same day)

Collaborators and Investigators

• Sponsor: University of Rochester
• Collaborators: National Cancer Institute (NCI); Rochester Dermatologic Surgery

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): July 1, 2027

Study Registration Dates

• First Submitted: June 25, 2026
• First Submitted That Met QC Criteria: June 30, 2026
• First Posted (Actual): July 6, 2026

Study Record Updates

• Last Update Posted (Actual): July 6, 2026
• Last Update Submitted That Met QC Criteria: June 30, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Keywords: machine learning; two-photon microscopy
• Additional Relevant MeSH Terms: Neoplasms; Neoplasms by Histologic Type; Neoplasms, Glandular and Epithelial; Carcinoma; Neoplasms, Squamous Cell; Neoplasms, Basal Cell; Carcinoma, Squamous Cell; Carcinoma, Basal Cell
• Other Study ID Numbers: STUDY00009823B; R37CA258376 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Deidentified sets of two-photon images and corresponding conventional histology will be made available upon request. Links to full resolution image data will be included in publications along with the results of machine learning analysis.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No