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Reflectance confocal microscopy (RCM) is a noninvasive optical imaging technique that uses a laser to capture cellular-level resolution images based on differing refractive indices of tissue elements. RCM image interpretation is challenging and requires training to interpret and correlate the grayscale output images that lack nuclear features with tissue pathology. Here, we utilize a deep learning-based framework that uses a convolutional neural network to transform grayscale images into virtually-stained hematoxylin and eosin (H&E)-like images enabling the visualization of various skin layers. To train the deep-learning framework, a series of a minimum of 7 time-lapsed, successive “stacks” of RCM images of excised tissue, spaced 1.52μm apart to a depth of 60.96μm were obtained using the Vivascope 1500. The tissue samples were stained with a 50% acetic acid solution to enhance cell nuclei. These images served as the “ground truth” to train a deep convolutional neural network with a conditional generative adversarial network (GAN)-based machine learning algorithm to digitally convert the images into GAN-based H&E-stained digital images. The machine learning algorithm was initially trained and subsequently retrained with new samples, specifically focusing on squamous neoplasms. The trained algorithm was applied to skin lesions that had a clinical differential diagnosis of squamous neoplasms including squamous cell carcinoma, actinic keratosis, seborrheic keratosis, and basal cell carcinoma. Through continuous training and refinement, the algorithm was able to produce high-resolution, histological quality images of different squamous neoplasms. This algorithm may be used in the future to facilitate earlier diagnosis of cutaneous neoplasms and enable greater uptake of noninvasive imaging technology within the medical community. 

Defining the presence of residual tumor and margins may enhance tissue sparing in dermatologic surgery, but no device serves this role. Reflectance Confocal Microscopy (RCM) provides non-invasive cellular-level resolution of the skin, but the FDA-approved RCM device is rigid and requires a tissue cap making tissue mapping difficult. We previously applied “virtual histology”, a deep-learning algorithm to RCM images to generate biopsy-free histology, however, whether virtual histology can be applied to images obtained with a portable, handheld RCM device to scan for residual tumor and margins is unknown. We hypothesize that combining a handheld device with virtual histology could provide accurate tumor assessment and these virtual histology images would correlate with traditional histology. The study was conducted as a prospective, consecutive non-randomized trial at a VA Medical Center dermatologic surgery clinic. Patients over 18 years old with confirmed BCC, SCC, or SCCis were included. Successive in-vivo confocal images from the epidermis and dermis were obtained with the handheld device and processed through a conditional generative adversarial network-based algorithm to create H&E pseudo-stained virtual histology. The algorithm produced similar virtual histology of in-vivo RCM images from the handheld and standard device, demonstrating successful application to the handheld device. Virtual histology applied to handheld RCM images capturing residual tumor, precancerous lesions (actinic keratosis) and scar tissue correlated with Mohs frozen section histology from excised tissue. The combination of machine-learning based virtual histology with handheld RCM images may provide histology-quality data in real time for tumor evaluation to assist the surgeon, improving clinical efficiency by decreasing unnecessary surgeries/layers and cosmesis through better margin assessment.