• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Sunday, February 8, 2026
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Health

MRI analysis with machine learning predicts impairment after spinal injury, study shows

Bioengineer by Bioengineer
April 5, 2018
in Health
Reading Time: 2 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

Leesburg, VA, April 2, 2018 – A test of machine-learning algorithms shows promise for computer-aided prognosis of acute spinal cord injury, according to a study to be presented at the ARRS 2018 Annual Meeting, set for April 22-27 in Washington, DC.

The study to be presented by Jason Talbot, assistant professor of radiology at the University of California, San Francisco, involved using semiautomated image analysis with machine-learning algorithms to assess the accuracy of axial T2-weighted radiomic features for classifying patients by degree of neurologic injury.

Several machine-learning algorithms were tested for injury classification based on texture variables. For each trained model, the accuracy of predicting the testing set was recorded, as were variables important to the model.

This proof-of-principle study highlights the feasibility of applying a semiautomated MRI analysis pipeline for atlas-based texture feature extraction from T2-weighted MRI at the epicenter of acute spinal cord injury (SCI). The results show that exploratory application of five machine-learning algorithms integrated into the analysis pipeline can classify patients by degree of neurologic impairment with variable accuracy and identify potential prognostic texture features. These data show promise for computer-aided prognosis of acute SCI.

###

With educational activities representing the entire spectrum of radiology, ARRS will host leading radiologists from around the world at the ARRS 2018 Annual Meeting, April 22-27, at the Marriott Wardman Park Hotel in Washington, DC. For more information, visit http://www.arrs.org/am18.

Founded in 1900, ARRS is the first and oldest radiology society in the United States and is an international forum for progress in radiology. The Society's mission is to improve health through a community committed to advancing knowledge and skills in radiology. ARRS achieves its mission through an annual scientific and educational meeting, publication of the American Journal of Roentgenology (AJR) and InPractice magazine, topical symposia and webinars, and print and online educational materials. ARRS is located in Leesburg, VA.

Media Contact

Mike Mason
[email protected]
703-858-4332
@ARRS_Radiology

http://www.arrs.org

http://www.arrs.org/ARRSLIVE/Pressroom/PressReleases/2018_04_04_01.aspx

Share12Tweet7Share2ShareShareShare1

Related Posts

Evaluating Biosimilar Trastuzumab for Breast Cancer in Thailand

February 8, 2026

Decoding Phantom Limb Movements via Intraneural Signals

February 8, 2026

Attitudes Toward Aging Impact Early Nursing Home Quality

February 8, 2026

Transforming Healthcare: Just Culture and Restorative Practices

February 8, 2026
Please login to join discussion

POPULAR NEWS

  • Robotic Ureteral Reconstruction: A Novel Approach

    Robotic Ureteral Reconstruction: A Novel Approach

    82 shares
    Share 33 Tweet 21
  • Digital Privacy: Health Data Control in Incarceration

    63 shares
    Share 25 Tweet 16
  • Breakthrough in RNA Research Accelerates Medical Innovations Timeline

    53 shares
    Share 21 Tweet 13
  • Mapping Tertiary Lymphoid Structures for Kidney Cancer Biomarkers

    50 shares
    Share 20 Tweet 13

About

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

Follow us

Recent News

Evaluating Biosimilar Trastuzumab for Breast Cancer in Thailand

Decoding Phantom Limb Movements via Intraneural Signals

Attitudes Toward Aging Impact Early Nursing Home Quality

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 73 other subscribers
  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

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

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

Bioengineer.org © Copyright 2023 All Rights Reserved.