A noninvasive optical technology based on deep learning presents a biopsy-free solution to skin disease diagnosis.

TOP INSIGHT
The optical images of unstained skin are transformed into virtually-stained volumetric images using a convolutional neural network.
Recently, a team of researchers using a deep learning framework developed RCM images to view intact skin without a biopsy.
However, the output of RCM images is not in a format that dermatologists and pathologists are familiar with, and analyzing these images requires specialized training since RCM images are in black and white, lack nuclear features, and reveal different planes within skin tissue compared to standard histology.
This technique, which the team calls “virtual histology”, allows analysis of microscopic images of the skin, bypasses several standard steps used for medical diagnosis, including skin biopsy, tissue fixation, processing, sectioning, as well as histochemical staining.
The research findings are published in the journal Light: Science & Applications.
The virtually-stained H&E images of unlabeled skin tissue showed similar color contrast and spatial features found in histochemically stained microscopic images of the biopsied tissue.
Source-Medindia
MEDINDIA




Email










