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
This trial does not accept healthy volunteersQualifiers
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
Design model
Single group
Treatments tested in this trial
Two photon microscopy imaging
DeviceEx vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis
Treatment groups
92 Participants
are divided into 1 treatment groupGroup A: TPFM imaging of biopsyExperimental treatment 1 intervention
Trial outcomes
Primary outcomes
1
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.
Time frame
During or immediately following patient biopsy (same day)2
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.
Time frame
During or immediately following patient biopsy (same day)Secondary outcomes
1
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.
Time frame
After completion of patient diagnosis (typically 1-2 weeks after procedure)2
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.
Time frame
During or immediately following patient biopsy (same day)Sponsors and contacts
Click on the lead sponsor to view all of their trials.
University of Rochester
Lead sponsor
National Cancer Institute (NCI)
Collaborator
Rochester Dermatologic Surgery
Collaborator
Source ClinicalTrials.gov