Artificial Intelligence Digitally Stains Tissue Samples Used in Pathology, Saving Labor, Time & Cost
However, this standard process of staining a tissue specimen is laborious, costly and requires a dedicated laboratory infrastructure, chemical reagents, as well as trained personnel (histotechnologists)
Researchers at UCLA have developed a deep learning-based method to take a microscopic image of naturally present fluorescent compounds in unstained tissue sections and digitally transform this "auto-fluorescence"
The success of this new virtual staining method was demonstrated for different stains and human tissue types, including sections of salivary gland, thyroid, kidney, liver and lung. The efficacy of the virtual staining process was independently evaluated by a panel of board-certified pathologists, who were blinded to the origin of the examined images such that the pathologists did not know which images were actually stained by an expert technician and which images were virtually stained by a neural network. The conclusion of this blinded study revealed no clinically significant difference in the staining quality and the medical diagnoses resulting from the two sets of images. This virtual staining process powered by deep learning will significantly reduce cost and sample preparation time, while also saving expert labor. Since it only requires a standard fluorescence microscope and a simple computer (such as a laptop), it is especially transformative for pathology needs in resource-limited settings and developing countries.
This research was published in Nature Biomedical Engineering, and was led by Dr. Aydogan Ozcan, the Chancellor's Professor of electrical and computer engineering at UCLA, and an associate director of the California NanoSystems Institute (CNSI), Dr. Yair Rivenson, an adjunct Professor of electrical and computer engineering at UCLA, along with UCLA graduate students, Hongda Wang, Kevin de Haan and Zhensong Wei. Clinical validation of this virtual staining method was directed by Dr. W. Dean Wallace from the Department of Pathology and Laboratory Medicine at the David Geffen School of Medicine at UCLA.
"This technology has the potential to fundamentally change the clinical histopathology workflow, by making tissue staining process extremely fast and simple, without the need for expert technicians or an advanced medical laboratory."
Another major impact of this virtual staining method is the standardization of the entire staining process since a trained neural network also eliminates the staining variability observed among technicians and medical laboratories, which can cause misdiagnoses and misclassification of biopsies.
The research of Ozcan Lab was supported by the Koc Group, NSF and HHMI.
Link to the published paper: https://www.nature.com/
Link to Ozcan Lab at UCLA: https://innovate.ee.ucla.edu/