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Related Concept Videos

Bones of the Upper Limb: Radius01:09

Bones of the Upper Limb: Radius

The radius is longer of the two bones that make up the human antebrachium or forearm. At the proximal end, the radius articulates with the capitulum of the humerus and the radial notch of the ulna to form the elbow joint. At the distal end, the radius articulates with the ulna via the ulnar notch, forming the distal radioulnar joint. Distally, the radius also attaches to the carpal wrist bones (scaphoid and lunate) to form the radiocarpal joint.
The radius has a nail-shaped head, and a short...
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Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Bones of the Upper Limb: Ulna01:15

Bones of the Upper Limb: Ulna

The ulna and radius are parallel bones of the antebrachium or the forearm. The ulna lies medially and consists of a bony tip called the olecranon process at its proximal end. This hook-like projection articulates with the olecranon fossa of the humerus and forms the "hinged" ulnohumeral part of the elbow joint. This joint facilitates forearm extension and flexion while preventing its hyperextension. Similarly, the coronoid process, another bony projection on the proximal/anterior side of the...

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Updated: Jun 13, 2026

Erosion Identification in Metacarpophalangeal Joints in Rheumatoid Arthritis using High-Resolution Peripheral Quantitative Computed Tomography
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Erosion Identification in Metacarpophalangeal Joints in Rheumatoid Arthritis using High-Resolution Peripheral Quantitative Computed Tomography

Published on: October 6, 2023

Radiograph-Based Deep Learning Model to Support Finger Joint Selection for Ultrasound Examination in Rheumatoid

Youngjae Park1, Keum San Chun2,3, Seungeun Lee2,3

  • 1Department of Rheumatology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.

Diagnostics (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model using radiographs can help doctors prioritize which finger joints to examine with ultrasound in rheumatoid arthritis (RA) patients. This AI tool significantly reduces the number of joints needing examination, improving efficiency in RA diagnosis.

Keywords:
deep-learninghandinflammationradiographyrheumatoid arthritis

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Last Updated: Jun 13, 2026

Erosion Identification in Metacarpophalangeal Joints in Rheumatoid Arthritis using High-Resolution Peripheral Quantitative Computed Tomography
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Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Rheumatology
  • Diagnostic Imaging

Background:

  • Ultrasound (US) is standard for evaluating rheumatoid arthritis (RA) joint inflammation.
  • Current US examination is operator-dependent and time-consuming.
  • Need for efficient prioritization of joints for US in RA patients.

Purpose of the Study:

  • Develop and evaluate a radiograph-based deep learning (DL) model.
  • Support prioritization of finger joints for US examination in RA.
  • Enhance joint-level US triage in clinical practice.

Main Methods:

  • Retrospective analysis of hand radiographs from RA patients with same-day US.
  • Developed a DL model using radiographs and clinical data (tenderness, swelling).
  • Compared DL model performance against a clinical-only logistic regression model.

Main Results:

  • DL model showed higher sensitivity (82.1% vs. 38.5%) and F1-score (69.6% vs. 48.4%) than the clinical model.
  • DL model demonstrated superior area under the precision-recall curve (0.625 vs. 0.540).
  • DL-assisted triage reduced US examination of finger joints by approximately 80%.

Conclusions:

  • A radiograph-based DL model effectively prioritizes finger joints for US in RA.
  • This AI approach offers a practical method to improve US triage efficiency.
  • The model supports routine clinical practice for RA joint assessment.