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

Changes in the Appendicular Skeleton with Age01:09

Changes in the Appendicular Skeleton with Age

The upper and lower limb initially develops as a small bulge called a limb bud, which appears on the lateral side of the early embryo. The upper limb bud appears near the end of the fourth week of development, with the lower limb bud appearing shortly after.
Initially, the limb buds consist of a core of mesenchyme covered by a layer of ectoderm. The ectoderm at the end of the limb bud thickens to form a narrow crest called the apical ectodermal ridge. This ridge stimulates the underlying...
Growth of Cartilage and Bone Tissue01:27

Growth of Cartilage and Bone Tissue

Chondrocytes form a temporary cartilaginous model by dividing and secreting a thick gel-like extracellular matrix. Once the chondrocytes undergo programmed cell death, osteoblasts enter the site of the cartilaginous model. The process of replacing the temporary cartilaginous model with bone in an ordered manner is called endochondral ossification. In endochondral ossification, not all of the cartilage is replaced by bone tissue. Some cartilage that performs a protective and supportive function...
Signs of Puberty01:27

Signs of Puberty

Puberty is a critical phase, typically beginning between the ages of 8 and 13 in girls and 9 and 14 in boys, though timing can vary based on genetics, environmental factors, and overall health. This period is characterized by the development of secondary sexual characteristics and the attainment of reproductive potential. Endocrine changes underpin puberty, with hormonal surges of Luteinizing Hormone (LH) and Follicle-Stimulating Hormone (FSH) instigated by Gonadotropin-Releasing Hormone (GnRH)...
Bone Formation by Endochondral Ossification01:24

Bone Formation by Endochondral Ossification

Bone formation, or ossification, begins around the sixth to seventh week of embryonic development. Most bones develop from a cartilaginous template through the process of endochondral ossification. Cartilage formation begins when clusters of mesenchymal cells differentiate into chondrocytes. These chondrocytes proliferate rapidly and secrete an extracellular matrix that becomes encased in a membrane called the perichondrium. The resulting cartilage model provides a template that resembles the...
Bone Disorders01:29

Bone Disorders

Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
Bone Remodeling01:40

Bone Remodeling

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.

You might also read

Related Articles

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

Sort by
Same author

Imaging Assessment and Management of Pediatric Thyroid Nodules: Emphasis on Application of 2021 K-TIRADS.

Journal of the Korean Society of Radiology·2026
Same author

Therapeutic effects of selumetinib on diffuse neurofibroma and optic pathway glioma in neurofibromatosis type 1.

Journal of neuro-oncology·2026
Same author

Ultrasonographic Evaluation of Pediatric Thyroid Nodules: Adult Risk Stratification Systems, 2021 K-TIRADS Revision, and Future Refinements.

Korean journal of radiology·2026
Same author

Caregiver quality of life and burden in rare genetic diseases in South Korea.

Medicine·2026
Same author

Establishing CT-derived Normative Liver and Spleen Volumes for Children: Validation in Regional and International Datasets.

Radiology·2026
Same author

Diagnostic criteria for acquired hypothalamic obesity - international expert guidance document.

Endocrine journal·2025

Related Experiment Video

Updated: Jun 3, 2026

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
07:56

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts

Published on: January 29, 2018

Deep Learning-Based Bone Age Assessment for Predicting Final Adult Height in Girls With Central Precocious Puberty.

Jeong Min Song1, Pyeong Hwa Kim1, Young Ah Cho1

  • 1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Korean Journal of Radiology
|June 1, 2026
PubMed
Summary

Artificial intelligence (AI) bone age assessments show accuracy comparable to human experts for predicting final adult height (FAH) in girls with central precocious puberty (CPP). The Bayley-Pinneau (BP) model demonstrated superior agreement for FAH prediction compared to the Korean National Growth Chart (KGC) model.

Keywords:
Body heightBone ageDeep learningGrowth predictionPrecocious puberty

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

Related Experiment Videos

Last Updated: Jun 3, 2026

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
07:56

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts

Published on: January 29, 2018

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

Area of Science:

  • Pediatric Endocrinology
  • Artificial Intelligence in Medicine
  • Growth and Development

Background:

  • Central precocious puberty (CPP) requires accurate prediction of final adult height (FAH) for treatment planning.
  • Traditional bone age assessment methods can have inter-observer variability.
  • Artificial intelligence (AI) offers potential for standardized and accurate bone age evaluation.

Purpose of the Study:

  • To evaluate the accuracy of AI-derived bone age assessments in predicting FAH in Korean girls with CPP.
  • To compare the performance of AI-based bone age methods with human expert assessment.
  • To assess the predictive accuracy of the Bayley-Pinneau (BP) and Korean National Growth Chart (KGC) models using different bone age inputs.

Main Methods:

  • Retrospective study of 122 Korean girls with CPP treated with gonadotropin-releasing hormone agonist (GnRHa).
  • Bone age assessed using human-based Greulich-Pyle (GP) atlas, AI-derived GP (AI-GP), and AI-weighted GP scoring (AI-GPw).
  • Predicted adult heights (PAHs) calculated using BP and KGC models at treatment initiation and completion; prediction accuracy analyzed.

Main Results:

  • AI-GP and AI-GPw showed comparable accuracy to human-GP in predicting FAH.
  • The BP model consistently demonstrated narrower limits of agreement than the KGC model for FAH prediction.
  • AI-weighted GP scoring combined with the BP model (AI-GPw-BP) was an independent predictor of FAH.

Conclusions:

  • AI-derived bone age assessments are clinically useful for predicting FAH in girls with CPP.
  • The BP model offers more consistent FAH prediction accuracy than the KGC model in this cohort.
  • AI-GPw-BP integration supports individualized growth prediction and treatment optimization in pediatric endocrinology.