Abstract Mohs micrographic surgery (Mohs) is the most effective method to treat nonmelanoma skin cancer. Mohs achieves high success (98% cure rates) by assessing surgical margins intraoperatively with frozen section histopathology. Unfortunately, the sophisticated infrastructure and laborious process needed to perform frozen section histopathology leads to lengthy, expensive surgeries that limit access and result in disparities of care. We propose to develop "optical Mohs" as a rapid, low-infrastructure alternative for Mohs-indicated patients in rural and other underserved populations who do not currently undergo Mohs surgery. Our optical Mohs approach will be based on multimodal confocal microscopy (MCM) combined with machine learning to provide a low infrastructure, automated diagnostic tool requiring minimal tissue processing. MCM combines reflectance, fluorescence, and Raman confocal microscopy into a single benchtop platform. MCM (using reflectance and fluorescence) has recently demonstrated success in producing H&E images of unprocessed, freshly excised skin that pathologist can read with accuracy comparable to frozen section histopathology. However, this approach alone still requires a pathologist to read the image. Machine learning is being explored to automate the diagnosis of these images, but has not yet yielded sufficient accuracy. We hypothesize that the addition of Raman spectroscopy will significantly increase the diagnostic accuracy of an automated approach. Raman is a complementary approach that is sensitive to the skin’s molecular composition and has been proven in clinical margin detection studies within the skin with sensitivities of 92-100% and specificities of 84-93%; however, a critical barrier to its adoption has been its slow acquisition speed. We introduce two innovations in Raman acquisition (superpixel and line scanning) that enable acquisition of Raman at speeds compatible with surgical guidance (speeds of 1cm2/min.). Our preliminary model in thirty patients demonstrates that a predictive model trained on both structural reflectance confocal images and biochemical information extracted from Raman images discriminates basal cell carcinoma from normal structures with very high accuracy, suggesting that optical Mohs could help dermatologists "keep cutting" as needed to remove the entire tumor (100% sensitivity) while not removing an excessive amount of healthy tissue (92% specificity). We will design, fabricate and bench-test an MCM instrument (Aim 1). We will design a decision-support system for tumor margin assessment based on MCM images using a post-surgery data set in 108 patients (Aim 2). We will determine the accuracy of the decision support system for tumor margin assessment based on MCM imaging in an intraoperative setting in 72 patients (Aim 3). The potential clinical outcome would demonstrate that an optical Mohs guided surgery could be used where conventional Mohs is indicated but not currently used, expanding...