Medical Design Briefs - January 2022 - 35

scars as well as multiple visits to doctors. It also can be costly for
patients and the health care system, " says Dr. Philip Scumpia,
assistant professor of dermatology and dermatopathology at the
David Geffen School of Medicine at UCLA and the West Los
Angeles Veterans Affairs Hospital and a member of the UCLA
Jonsson Comprehensive Cancer Center. " Our approach potentially
offers a biopsy-free solution, providing images of skin structure
with cellular-level resolution. "
The research team, led by Ozcan, Scumpia and Dr. Gennady
Rubinstein, a dermatologist at the Dermatology & Laser
Centre in Los Angeles, created a deep-learning framework to
transform images of intact skin acquired by an emerging noninvasive
optical technology, reflectance confocal microscopy
(RCM), into a format that is user-friendly for dermatologists and
pathologists. Analyzing RCM images requires special training
because they are in black and white, and unlike standard histology,
they lack nuclear features of cells.
" I was surprised to see how easy it is for this virtual staining technology
to transform the images into ones that I typically see of skin
biopsies that are processed using traditional chemical fixation and
tissue staining under a microscope, " Scumpia says.
The researchers trained a " convolutional neural network " to
rapidly transform RCM images of unstained skin into virtually
stained 3D images like the H&E (hematoxylin and eosin)
images familiar to dermatologists and dermatopathologists.
Deep learning, a form of machine learning, constructs artificial
neural networks that, like the human brain, can " learn "
from large amounts of data.
" This framework can perform virtual histology on a variety
of skin conditions, including basal cell carcinoma. It also provides
detailed 3D images of several skin layers, " says Ozcan,
who also has UCLA faculty appointments in bioengineering
and surgery and is an associate director of the California
NanoSystems Institute. " In our studies, the virtually stained
images showed similar color contrast and spatial features
found in traditionally stained microscopic images of biopsied
tissue. This approach may allow diagnosticians to see the overall
histological features of intact skin without invasive skin
biopsies or the time-consuming work of chemical processing
and labeling of tissue. "
According to Rubinstein, this is an exciting proof-of-concept
study. " The only tool currently used in clinics to help a
Reflectance
confocal microscope
(RCM) imaging
RCM image
stacks
Acetic acid
virtual
staining
network
Acetic acid
virtually stained
tissue images
PseudoH&E
virtual
staining
network
Pseudo-H&E
virtually stained
tissue images
dermatologist are dermatoscopes, which magnify skin and
polarize light. At best, they can help a dermatologist pick up
patterns, " says Rubinstein, who also uses reflectance confocal
microscopes in clinic.
The authors say several steps remain in translating this technology
for clinical use, but their goal is to provide virtual histology
technology that can be built into any device - large,
small or combined with other optical-imaging systems. Once
the neural network is " trained, " with many tissue samples and
the use of powerful graphics processing units (GPUs), it will be
able to run on a computer or network, enabling rapid transformation
from a standard image to a virtual histology image.
Future studies will determine if this digital, biopsy-free
approach can interface with whole-body imaging and electronic
medical records to usher in a new age of " digital dermatology "
and change how dermatology is practiced. Additionally, the
research team will determine if this artificial intelligence platform
can work with other AI technologies to look for patterns and further
aid in clinical diagnosis.
Other authors of this work include electrical and computer
engineering assistant adjunct professor Yair Rivenson of UCLA
Samueli and Ozcan's engineering graduate students: Jingxi Li,
Jason Garfinkel, Xiaoran Zhang, Di Wu, Yijie Zhang, Kevin de
Haan, Hongda Wang, Tairan Liu, and Bijie Bai.
The research is funded by the National Science Foundation,
Biophotonics Program (PI: Ozcan). Scumpia, in collaboration
with Ozcan, received a VA Merit Award to further study biopsyfree
virtual histology in non-melanoma skin cancer in veterans.
For more information, visit https://samueli.ucla.edu.
Make your machine move
your ma
MICRO LINEAR ACTUATORS
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UCLA team achieves biopsy-free virtual histology of skin using deep learning
and RCM. (Credit: Aydogan Ozcan)
Medical Design Briefs, January 2022
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Medical Design Briefs - January 2022

Table of Contents for the Digital Edition of Medical Design Briefs - January 2022

Medical Design Briefs - January 2022 - Intro
Medical Design Briefs - January 2022 - Sponsor
Medical Design Briefs - January 2022 - Cov1a
Medical Design Briefs - January 2022 - Cov1b
Medical Design Briefs - January 2022 - Cov1
Medical Design Briefs - January 2022 - Cov2
Medical Design Briefs - January 2022 - 1
Medical Design Briefs - January 2022 - 2
Medical Design Briefs - January 2022 - 3
Medical Design Briefs - January 2022 - 4
Medical Design Briefs - January 2022 - 5
Medical Design Briefs - January 2022 - 6
Medical Design Briefs - January 2022 - 7
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Medical Design Briefs - January 2022 - 40
Medical Design Briefs - January 2022 - Cov3
Medical Design Briefs - January 2022 - Cov4
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