Jin, LingboTang, YuboCoole, Jackson B.Tan, Melody T.Zhao, XuanBadaoui, HawraaRobinson, Jacob T.Williams, Michelle D.Vigneswaran, NadarajahGillenwater, Ann M.Richards-Kortum, Rebecca R.Veeraraghavan, Ashok2024-07-252024-07-252024Jin, L., Tang, Y., Coole, J. B., Tan, M. T., Zhao, X., Badaoui, H., Robinson, J. T., Williams, M. D., Vigneswaran, N., Gillenwater, A. M., Richards-Kortum, R. R., & Veeraraghavan, A. (2024). DeepDOF-SE: Affordable deep-learning microscopy platform for slide-free histology. Nature Communications, 15(1), 2935. https://doi.org/10.1038/s41467-024-47065-2https://hdl.handle.net/1911/117521Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.engExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Journal articles41467-024-47065-2https://doi.org/10.1038/s41467-024-47065-2