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

11.7K
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...
11.7K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

60.1K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
60.1K
Bone Remodeling01:40

Bone Remodeling

40.9K
Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.
40.9K

You might also read

Related Articles

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

Sort by
Same author

Flavorzyme Hydrolyzed Cordyceps militaris Mushroom Enhanced Quality and Functional Properties of Chicken Breast Meat during Storage.

Food science of animal resources·2026
Same author

Flavorzyme Hydrolyzed <i>Cordyceps militaris</i> Mushroom Enhanced Quality and Functional Properties of Chicken Breast Meat during Storage.

Food science of animal resources·2026
Same author

Subacute respiratory symptoms in a patient with Crohn's disease and ankylosing spondylitis.

The Korean journal of internal medicine·2026
Same author

Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions.

Frontiers in plant science·2026
Same author

Associations Between Accelerometer-Measured 24-Hour Movement Behaviors and Cardiac Conduction Disease in the UK Biobank Cohort.

Korean circulation journal·2026
Same author

Rapid feed component assessment to enhance livestock productivity and reduce emissions.

Scientific reports·2025

Related Experiment Video

Updated: Mar 20, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

262

Development of a smartphone-based bone maturity classification algorithm with XAI for beef carcass grading.

Juntae Kim1, Sung-Hwan Ahn2, Suk-Ki Yoon2

  • 1Department of Smart Agriculture Systems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea.

Food Science of Animal Resources
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a smartphone-based deep learning method for objectively assessing cattle cartilage ossification, improving beef grading accuracy. Object detection models achieved over 95% accuracy, with YOLO v9m and v10m showing the highest performance.

Keywords:
Beef carcass maturity gradingExplainable artificial intelligenceObject grading systemOssificationYOLO model

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

1.3K

Related Experiment Videos

Last Updated: Mar 20, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

262
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

1.3K

Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Korean beef grading relies on marbling and loin-eye area, but cartilage ossification also impacts quality.
  • Traditional visual assessment of ossification is subjective and inconsistent, especially for older cows.
  • Objective, image-based methods are needed to enhance the accuracy and reliability of beef carcass grading.

Purpose of the Study:

  • To develop and evaluate a smartphone-based deep learning approach for assessing cattle cartilage ossification levels.
  • To compare the performance of various object detection models (YOLO v8-v11) for ossification grading.
  • To validate the model's focus on relevant anatomical regions using explainable AI (XAI) techniques.

Main Methods:

  • Collected 1,770 smartphone images per ossification grade (6-9).
  • Trained and compared YOLO v8, v9, v10, and v11 object detection models.
  • Applied Grad-CAM and LIME (XAI) to interpret model predictions.

Main Results:

  • All YOLO models achieved over 95% accuracy in grading ossification levels.
  • YOLO v9m and YOLO v10m demonstrated the highest accuracy at 99.08% and 99.22%, respectively.
  • XAI techniques confirmed models focused on key areas related to carcass maturity.

Conclusions:

  • Smartphone-based deep learning offers an efficient and reliable method for cartilage ossification assessment.
  • The proposed approach significantly improves grading accuracy and consistency in meat processing.
  • This technology supports the adoption of objective grading practices in the beef industry.