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

X-ray Imaging01:24

X-ray Imaging

6.6K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
6.6K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

429
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
429
Ultrasonography01:17

Ultrasonography

5.5K
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.
During an ultrasonography procedure, a handheld device called...
5.5K
Computed Tomography01:10

Computed Tomography

5.8K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
5.8K
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

6.4K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
6.4K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

45
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
45

You might also read

Related Articles

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

Sort by
Same author

Federated Computing in Orthopaedic Surgery: A Paradigm Shift in Collaborative Multicenter Research.

The Journal of bone and joint surgery. American volume·2026
Same author

Letter to the Editor: Editorial: AAOS Orthobiologics Registry-Sometimes, More is Less.

Clinical orthopaedics and related research·2026
Same author

Beyond Awareness: Moving from Knowledge to Action in Operating Room Sustainability: Commentary on an article by Laura L. Bellaire, MD, and Isabelle Freiling, PhD: "Effective Communication Strategies to Address Excessive Waste and Overconsumption in the Orthopaedic Operating Room".

The Journal of bone and joint surgery. American volume·2025
Same author

CORR Insights®: Statistical Groupings of Mental Health and Osteoarthritis Severity Correlate With 10-year Trajectories of Levels of Capability and Comfort Among People With Hip Pain: A Nationwide Prospective Cohort Study (CHECK).

Clinical orthopaedics and related research·2025
Same author

The Challenges of Using ChatGPT for Clinical Decision Support in Orthopaedic Surgery: A Pilot Study.

The Journal of the American Academy of Orthopaedic Surgeons·2025
Same author

The Discordance Between Pain and Imaging in Knee Osteoarthritis.

The Journal of the American Academy of Orthopaedic Surgeons·2025

Related Experiment Video

Updated: Sep 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Deep Learning and Imaging for the Orthopaedic Surgeon: How Machines "Read" Radiographs.

Brandon G Hill1, Justin D Krogue2,3, David S Jevsevar1,4

  • 1Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire.

The Journal of Bone and Joint Surgery. American Volume
|July 22, 2022
PubMed
Summary

Deep learning is revolutionizing medical imaging analysis in orthopaedics. This technology shows expert-level performance in identifying fractures, paving the way for automated interpretation in clinical settings.

More Related Videos

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

972

Related Experiment Videos

Last Updated: Sep 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

972

Area of Science:

  • Orthopaedic Surgery
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Deep learning is rapidly advancing medical image analysis across various modalities used in orthopaedics.
  • This technology demonstrates significant progress in interpreting radiographs, CT scans, and MRI scans.

Purpose of the Study:

  • To provide orthopaedic surgeons with a conceptual understanding of deep learning.
  • To prepare surgeons for the integration of automated medical image interpretation technologies.

Main Methods:

  • Review of current deep learning applications in musculoskeletal radiography.
  • Analysis of studies demonstrating deep learning's performance in fracture identification and localization.

Main Results:

  • Deep learning models achieve expert or near-expert performance in identifying and localizing fractures on radiographs.
  • Evidence of clinical utility for deep learning in musculoskeletal radiology is growing.

Conclusions:

  • Deep learning is in the early stages of clinical adoption, with ongoing validation and proof-of-concept studies.
  • Understanding deep learning's fundamental principles is crucial for surgeons as the technology becomes integrated into clinical practice.