• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Wednesday, August 6, 2025
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

AI abdominal fat measure predicts heart attack and stroke

Bioengineer by Bioengineer
December 2, 2020
in Science News
Reading Time: 3 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

IMAGE

Credit: Radiological Society of North America

OAK BROOK, Ill. – Automated deep learning analysis of abdominal CT images produces a more precise measurement of body composition and predicts major cardiovascular events, such as heart attack and stroke, better than overall weight or body mass index (BMI), according to a study presented today at the annual meeting of the Radiological Society of North America (RSNA).

“Established cardiovascular risk models rely on factors like weight and BMI that are crude surrogates of body composition,” said Kirti Magudia, M.D., Ph.D., an abdominal imaging and ultrasound fellow at the University of California San Francisco. “It’s well established that people with the same BMI can have markedly different proportions of muscle and fat. These differences are important for a variety of health outcomes.”

Unlike BMI, which is based on height and weight, a single axial CT slice of the abdomen visualizes the volume of subcutaneous fat area, visceral fat area and skeletal muscle area. However, manually measuring these individual areas is time intensive and costly.

As a radiology resident at Brigham and Women’s Hospital in Boston, Dr. Magudia was part of a multidisciplinary team of researchers, including radiologists, a data scientist and biostatistician, who developed a fully automated method using deep learning–a type of artificial intelligence (AI)–to determine body composition metrics from abdominal CT images.

“Abdominal CT scans that are routinely performed provide a more granular way of looking at body composition, but we’re not currently taking advantage of it,” Dr. Magudia said.

The study cohort was derived from the 33,182 abdominal CT outpatient exams performed on 23,136 patients at Partners Healthcare in Boston in 2012. The researchers identified 12,128 patients who were free of major cardiovascular and cancer diagnoses at the time of imaging. Mean age of the patients was 52 years, and 57% of patients were women.

The researchers selected the L3 CT slice (from the third lumbar spine vertebra) and calculated body composition areas for each patient. Patients were then divided into four quartiles based on the normalized values of subcutaneous fat area, visceral fat area and skeletal muscle area.

In this retrospective study, it was determined which of these 12,128 patients had a myocardial infarction (heart attack) or stroke within 5 years after their index abdominal CT scan. The researchers found 1,560 myocardial infarctions and 938 strokes occurred in this study group.

Statistical analysis demonstrated that visceral fat area was independently associated with future heart attack and stroke. BMI was not associated with heart attack or stroke.

“The group of patients with the highest proportion of visceral fat area were more likely to have a heart attack, even when adjusted for known cardiovascular risk factors,” said Dr. Magudia. “The group of patients with the lowest amount of visceral fat area were protected against stroke in the years following the abdominal CT exam.”

“These results demonstrate that precise measures of body muscle and fat compartments achieved through CT outperform traditional biomarkers for predicting risk for cardiovascular outcomes,” she added.

According to Dr. Magudia, this work demonstrates that fully automated and normalized body composition analysis could now be applied to large-scale research projects.

“This work shows the promise of AI systems to add value to clinical care by extracting new information from existing imaging data,” Dr. Magudia said. “The deployment of AI systems would allow radiologists, cardiologists and primary care doctors to provide better care to patients at minimal incremental cost to the health care system.”

This paper is the recipient of an RSNA 2020 Trainee Research Prize.

Co-authors are Christopher P. Bridge, D.Phil., Camden P. Bay, Ph.D., Florian J. Fintelmann, M.D., Ana Babic, Ph.D., Katherine P. Andriole, Ph.D., Brian M. Wolpin, M.D., and Michael H. Rosenthal, M.D., Ph.D.

###

For more information and images, visit RSNA.org/press20. Press account required to view embargoed materials.

RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois. (RSNA.org)

Editor’s note: The data in these releases may differ from those in the published abstract and those actually presented at the meeting, as researchers continue to update their data right up until the meeting. To ensure you are using the most up-to-date information, please call the RSNA media relations team at Newsroom at 1-630-590-7762.

For patient-friendly information on abdominal CT, visit RadiologyInfo.org.

Media Contact
Linda Brooks
[email protected]

Original Source

https://press.rsna.org/timssnet/media/pressreleases/14_pr_target.cfm?ID=2235

Tags: CardiologyDiagnosticsInternal MedicineMedicine/HealthRobotry/Artificial IntelligenceStroke
Share12Tweet8Share2ShareShareShare2

Related Posts

Multiplex Assay Detects HIV-1, HBV, and STRs

Multiplex Assay Detects HIV-1, HBV, and STRs

August 6, 2025
GABA Best Detects Early Parkinson’s Changes with RBD

GABA Best Detects Early Parkinson’s Changes with RBD

August 6, 2025

Flavor and Bioactive Potential of Roasted Rice Bran Oil

August 5, 2025

New Research from Pitt Reveals Potential of Cellphone Data in Diagnosing and Treating Mental Health Disorders

August 5, 2025
Please login to join discussion

POPULAR NEWS

  • blank

    Neuropsychiatric Risks Linked to COVID-19 Revealed

    73 shares
    Share 29 Tweet 18
  • Overlooked Dangers: Debunking Common Myths About Skin Cancer Risk in the U.S.

    61 shares
    Share 24 Tweet 15
  • Predicting Colorectal Cancer Using Lifestyle Factors

    46 shares
    Share 18 Tweet 12
  • Dr. Miriam Merad Honored with French Knighthood for Groundbreaking Contributions to Science and Medicine

    47 shares
    Share 19 Tweet 12

About

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

Follow us

Recent News

Multiplex Assay Detects HIV-1, HBV, and STRs

GABA Best Detects Early Parkinson’s Changes with RBD

Flavor and Bioactive Potential of Roasted Rice Bran Oil

  • 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.