Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.2K
Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies

408
The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
408

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Predictive value of aorta enhancement on computed tomographic pulmonary angiography in pulmonary embolism.

PloS one·2025
Same author

Automatic joint inflammation estimation based on regression neural networks.

Medical physics·2025
Same author

Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.

Scientific reports·2024
Same author

Accuracy and precision of Volumetric Matching Micromotion Analysis (V3MA) is similar to RSA for tibial component migration in TKA.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society·2024
Same author

Using 3D point cloud and graph-based neural networks to improve the estimation of pulmonary function tests from chest CT.

Computers in biology and medicine·2024
Same author

Deep learning in rheumatological image interpretation.

Nature reviews. Rheumatology·2024
Same journal

Autoantibody associations in patients with early diffuse cutaneous systemic sclerosis: the prospective registry of early systemic sclerosis.

Seminars in arthritis and rheumatism·2026
Same journal

Effectiveness and safety of primary thromboprophylaxis in antiphospholipid antibody carriers by serological 2023 ACR/EULAR domains.

Seminars in arthritis and rheumatism·2026
Same journal

Clustering-based stratification of fibromyalgia subtypes: A comparative analysis of medicated and non-medicated cohorts from two academic centers.

Seminars in arthritis and rheumatism·2026
Same journal

Imaging techniques for assessing the hand in systemic sclerosis: a systematic review.

Seminars in arthritis and rheumatism·2026
Same journal

Evaluating unsupervised and rule-based phenotyping methods versus administrative code counts for systemic sclerosis identification.

Seminars in arthritis and rheumatism·2026
Same journal

Associations between MRI features and pain in first metatarsophalangeal joint osteoarthritis.

Seminars in arthritis and rheumatism·2026
See all related articles

Related Experiment Video

Updated: Jan 3, 2026

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis
06:31

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

Published on: October 6, 2023

2.9K

Artificial intelligence in detecting early RA.

Berend C Stoel1

  • 1Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.

Seminars in Arthritis and Rheumatism
|November 30, 2019
PubMed
Summary
This summary is machine-generated.

Early detection of Rheumatoid Arthritis (RA) is crucial for preventing chronic disease. Artificial intelligence applied to MRI scans shows promise for identifying subtle inflammatory changes, aiding in very early RA diagnosis and treatment.

More Related Videos

Preliminary Study on Acupuncture Combined with Grain-sized Moxibustion for Treating Rheumatoid Arthritis with Finger Joint Pain
04:50

Preliminary Study on Acupuncture Combined with Grain-sized Moxibustion for Treating Rheumatoid Arthritis with Finger Joint Pain

Published on: May 16, 2025

753
An Adoptive Transfer Model of Rheumatoid Arthritis in Mice
07:37

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice

Published on: June 6, 2025

921

Related Experiment Videos

Last Updated: Jan 3, 2026

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis
06:31

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

Published on: October 6, 2023

2.9K
Preliminary Study on Acupuncture Combined with Grain-sized Moxibustion for Treating Rheumatoid Arthritis with Finger Joint Pain
04:50

Preliminary Study on Acupuncture Combined with Grain-sized Moxibustion for Treating Rheumatoid Arthritis with Finger Joint Pain

Published on: May 16, 2025

753
An Adoptive Transfer Model of Rheumatoid Arthritis in Mice
07:37

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice

Published on: June 6, 2025

921

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Rheumatology

Background:

  • Early detection of Rheumatoid Arthritis (RA) is essential to prevent chronicity and improve patient outcomes.
  • Magnetic Resonance Imaging (MRI) can detect subclinical inflammation in the wrist, hand, and foot, aiding in early RA diagnosis.
  • Current manual quantification and visual scoring of MRI data are impractical and lack sensitivity for detecting subtle early changes.

Purpose of the Study:

  • To explore the application of artificial intelligence (AI), specifically deep learning, for quantifying early Rheumatoid Arthritis (RA) from MRI data.
  • To investigate patterns in large MRI datasets from healthy controls and at-risk patients for identifying RA development.
  • To assess the potential of AI-analyzed MRI as an outcome measure in clinical trials for early RA treatment.

Main Methods:

  • Review of the background and history of AI, with a focus on deep learning techniques.
  • Discussion of applying AI to analyze large volumes of MRI data from patients with arthralgia suspicious for RA.
  • Exploration of AI's capability to detect subtle inflammatory changes in MRI for early RA detection and treatment monitoring.

Main Results:

  • AI and deep learning have demonstrated potential in medical image analysis, sometimes outperforming human observers.
  • AI techniques offer a path towards overcoming the limitations of manual quantification and visual scoring in early RA detection via MRI.
  • The study highlights the potential for AI to analyze complex MRI patterns indicative of early RA development.

Conclusions:

  • AI, particularly deep learning, holds significant promise for the sensitive quantification of early Rheumatoid Arthritis (RA) using MRI data.
  • This approach could facilitate earlier diagnosis, enabling timely intervention to prevent disease chronicity.
  • AI-powered MRI analysis may serve as a valuable tool for monitoring treatment efficacy in clinical trials for early RA.