Clinical trial · Interventional
Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
Machine Learning Analysis of Expanded Two-photon Imaging of Skin Biopsy Specimens
NCT07682831CI-TRIAL-00122850recruitingN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Basal Cell Carcinoma of Skin | Skin Basal Cell Carcinoma | ALIAS | 0.90 |
| Squamous Cell Carcinoma (Skin) | Skin Squamous Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Two photon microscopy imaging | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- TPFM imaging of biopsy
- description
- Specimens will be imaged with TPFM and diagnosed using a machine learning model
- interventionNames
- Device: Two photon microscopy imaging
Primary outcomes (2)
- measure
- Sensitivity of Machine Learning Analysis of Two Photon Fluorescence Microscopy Images At Point of Care
- timeFrame
- During or immediately following patient biopsy (same day)
- description
- 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.
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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
References
Publications (0)
Data not yet available
No reference posted for this study.