Machine Learning Analysis of Two-photon Fluorescence Microscopy of Dermatologic Biopsies
Purpose
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)
Eligibility
- Eligible Ages
- All ages
- Eligible Sex
- All
- Accepts Healthy Volunteers
- No
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 Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- N/A
- Intervention Model
- Single Group Assignment
- Primary Purpose
- Diagnostic
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Experimental TPFM imaging of biopsy |
Specimens will be imaged with TPFM and diagnosed using a machine learning model |
|
Recruiting Locations
Victor, New York 14654
More Details
- Status
- Recruiting
- Sponsor
- University of Rochester
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.