• HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
Monday, January 18, 2021
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

AI-based ‘OxyGAN’ is a robust, effective method to measure tissue oxygen levels

Bioengineer by Bioengineer
December 1, 2020
in Chemistry
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

New AI-based algorithm processes tissue oxygenation data faster and more accurately than conventional techniques

IMAGE

Credit: SPIE

Tissue oxygenation is a measure of the oxygen level in biological tissue and is a useful clinical biomarker for tissue viability. Abnormal levels may indicate the presence of conditions such as sepsis, diabetes, viral infection, or pulmonary disease, and effective monitoring is important for surgical guidance as well as medical care.

Several techniques exist for the measurement of tissue oxygenation, but they all have some limitations. For instance, pulse oximetry is robust and low-cost but cannot provide a localized measure of oxygenation. Near-infrared spectroscopy, on the other hand, is prone to noisy measurements due to pressure-sensitive contact probes. Spatial frequency domain imaging (SFDI) has emerged as a promising noncontact technique that maps tissue oxygen concentrations over a wide field of view. While simple to implement, SFDI has its own limitations: it requires a sequence of several images for its predictions to be accurate and is prone to errors when working with single snapshots.

In a new study published in the Journal of Biomedical Optics, researchers from Johns Hopkins University, Mason T. Chen and Nicholas J. Durr, have proposed an end-to-end technique for accurate calculation of tissue oxygenation from single snapshots, called OxyGAN. They developed this approach using a class of machine-learning framework called a conditional generative adversarial network (cGAN), which utilizes two neural networks — a generator and a discriminator — simultaneously on the same input data. The generator learns to produce realistic output images, while the discriminator learns to determine whether a given image pair forms a correct reconstruction for a given input.

Using conventional SDFI, the researchers obtained oxygenation maps for the human esophagus (ex vivo), hands and feet (in vivo), and a pig colon (in vivo) under illumination with two different wavelengths (659 and 851 nm). They trained OxyGAN with the feet and esophagus samples and saved the hand and colon samples to later test its performance. Further, they compared its performance with a single-snapshot technique based on a physical model and a two-step hybrid technique that consisted of a deep-learning model to predict optical properties and a physical model to calculate tissue oxygenation.

The researchers found that OxyGAN could measure oxygenation accurately, not only for the samples it had seen during training (human feet), but also for the samples it had not seen (human hand and pig colon), demonstrating the robustness of the model. It performed better than both the single-snapshot model and the hybrid model by 24.9% and 24.7%, respectively. Moreover, the scientists optimized OxyGAN to compute ~10 times faster than the hybrid model, enabling real-time mapping at a rate of 25 Hz. Frédéric Leblond, Associate Editor for the Journal of Biomedical Optics, comments, “Not only does this paper represent significant advances that can contribute to the practical clinical implementation of spatial frequency domain imaging, but it will also be part of a relatively small (although rapidly increasing in size) pool of robust published work using AI-type methods to deal with real biomedical optics data.”

While the algorithm of OxyGAN could be optimized further, this approach holds promise as a novel technique to measure tissue oxygenation.

###

Read the original research article by M. T. Chen and N. J. Durr, “Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning,” J. Biomed. Opt. 25(11), 112907 (2020), doi: 10.1117/1.JBO.25.11.112907.

Media Contact
Daneet Steffens
[email protected]

Original Source

https://spie.org/news/ai-based-oxygan-?SSO=1

Related Journal Article

http://dx.doi.org/10.1117/1.JBO.25.11.112907

Tags: Biomechanics/BiophysicsBiotechnologyMedicine/HealthOpticsResearch/DevelopmentRobotry/Artificial IntelligenceTechnology/Engineering/Computer Science
Share12Tweet7Share2ShareShareShare1

Related Posts

IMAGE

Synthesis of potent antibiotic follows unusual chemical pathway

January 18, 2021
IMAGE

A ‘super-puff’ planet like no other

January 18, 2021

Better diet and glucose uptake in the brain lead to longer life in fruit flies

January 16, 2021

Howard University professor to receive first Joseph A. Johnson Award

January 15, 2021
Next Post
IMAGE

Telomere shortening protects against cancer

IMAGE

Study finds false widow spiders bite can transmit harmful antibiotic-resistant bacteria

Leave a Reply Cancel reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

POPULAR NEWS

  • IMAGE

    The map of nuclear deformation takes the form of a mountain landscape

    53 shares
    Share 21 Tweet 13
  • Blood pressure drug may be key to increasing lifespan, new study shows

    44 shares
    Share 18 Tweet 11
  • New drug form may help treat osteoporosis, calcium-related disorders

    39 shares
    Share 16 Tweet 10
  • People living with HIV face premature heart disease and barriers to care

    57 shares
    Share 23 Tweet 14

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Tags

BiologyTechnology/Engineering/Computer SciencecancerChemistry/Physics/Materials SciencesEcology/EnvironmentGeneticsMedicine/HealthMaterialsClimate ChangeInfectious/Emerging DiseasesPublic HealthCell Biology

Recent Posts

  • New management approach can help avoid species vulnerability or extinction
  • New computational tool reliably differentiates between cancer and normal cells from single-cell RNA-sequencing data
  • Inexpensive battery charges rapidly for electric vehicles, reduces range anxiety
  • Timing is of the essence when treating brain swelling in mice
  • Contact Us

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

Welcome Back!

Login to your account below

Forgotten Password?

Create New Account!

Fill the forms below to register

All fields are required. Log In

Retrieve your password

Please enter your username or email address to reset your password.

Log In