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

Classification of Bones01:18

Classification of Bones

7.5K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
7.5K
X-ray Imaging01:24

X-ray Imaging

7.8K
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...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Building a Clinically Relevant and Technically Robust Synthetic Histopathology Dataset for Breast and Gastric Cancer.

Journal of medical systems·2026
Same author

Radiomics-Based Machine Learning for Sarcopenia Detection in Abdominal and Low-Dose CT.

Diagnostics (Basel, Switzerland)·2026
Same author

Biomechanical stability evaluation of isolated straddle fracture using finite element analysis.

Scientific reports·2026
Same author

Deep learning-based automatic scoring of drug-induced sleep endoscopy in obstructive sleep apnea.

NPJ digital medicine·2026
Same author

Deep Learning-Based Prediction System for Surgical Difficulty in Rectal Cancer Patients Using MRI Pelvimetry.

Yonsei medical journal·2026
Same author

A Novel Fluorescence-Triggered Auditory Feedback Photosensor for Precision Lymph Node Mapping.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Sep 21, 2025

Assessment of Bone Fracture Healing Using Micro-Computed Tomography
12:04

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Published on: December 9, 2022

2.0K

Automatically Diagnosing Skull Fractures Using an Object Detection Method and Deep Learning Algorithm in Plain

Tae Seok Jeong1, Gi Taek Yee2, Kwang Gi Kim3

  • 1Department of Traumatology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.

Journal of Korean Neurosurgical Society
|June 2, 2022
PubMed
Summary
This summary is machine-generated.

A deep learning object detection algorithm shows promise for identifying skull fractures in X-rays. This artificial intelligence tool can aid radiologists in diagnosing fractures, improving clinical applicability.

Keywords:
Artificial intelligenceDeep learningRadiographySkull fracturesTraumatic brain injury

More Related Videos

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
09:34

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

7.5K
Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

198

Related Experiment Videos

Last Updated: Sep 21, 2025

Assessment of Bone Fracture Healing Using Micro-Computed Tomography
12:04

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Published on: December 9, 2022

2.0K
A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
09:34

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

7.5K
Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

198

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning, a subset of machine learning, utilizes artificial neural networks for image analysis.
  • Object detection algorithms powered by deep learning are advanced tools for interpreting medical images.

Purpose of the Study:

  • To evaluate the diagnostic performance of a deep learning algorithm for detecting skull fractures on plain radiographic images.
  • To assess the clinical applicability of this artificial intelligence approach in fracture identification.

Main Methods:

  • A dataset of 2026 skull X-rays (991 fractures, 1035 normal) from 741 patients was analyzed.
  • The RetinaNet architecture was employed as the deep learning model.
  • Diagnostic performance was quantified using precision, recall, and average precision metrics.

Main Results:

  • The deep learning model achieved an average precision of 0.7240 at an Intersection over Union (IOU) threshold of 0.1.
  • At an IOU of 0.1 and confidence threshold of 0.6, the algorithm demonstrated an 82.9% true detection rate.
  • Performance varied across radiographic views, with challenges in detecting certain fracture types (e.g., diastatic, suture line fractures).

Conclusions:

  • Deep learning-based object detection algorithms represent a valuable tool for skull fracture diagnosis.
  • Further refinement may improve detection of subtle fractures and enhance clinical utility.