University of Rochester
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?
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 learnin…
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
Ex vivo tissues will be imaged with two-photon microscopy and analyzed with machine learning for diagnosis
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