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Classification of Bones01:18

Classification of Bones

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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...
8.1K
Bone Remodeling01:40

Bone Remodeling

38.7K
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.
38.7K
Bone Structure01:55

Bone Structure

49.6K
Within the skeletal system, the structure of a bone, or osseous tissue, can be exemplified in a long bone, like the femur, where there are two types of osseous tissue: cortical and cancellous.
49.6K
Changes in the Appendicular Skeleton with Age01:09

Changes in the Appendicular Skeleton with Age

2.8K
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...
2.8K
Gross Anatomy of Bone01:17

Gross Anatomy of Bone

7.2K
The two main features of a long bone are the diaphysis and the epiphysis.
The diaphysis is the tubular shaft that runs between the proximal and distal ends of the bone. The walls of the diaphysis are composed of dense and hard compact bone made of numerous osteons — the functional unit of the compact bone. The hollow region in the diaphysis is called the medullary cavity, which harbors the bone marrow. In infants and children, this marrow cavity is filled with red marrow, whereas in...
7.2K
Bone Formation by Intramembranous Ossification01:29

Bone Formation by Intramembranous Ossification

8.3K
Intramembranous ossification is one of the two processes involved in the development of bones within an embryo. The flat bones of the face, most of the cranial bones, and the clavicles are formed via this process. During intramembranous ossification, the bones develop directly from sheets of undifferentiated mesenchymal connective tissue.
The process begins when mesenchymal cells in the embryonic skeleton gather together and differentiate into osteogenic cells, which then develop into ...
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Related Experiment Video

Updated: Oct 10, 2025

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

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Deep Learning Framework for Automatic Bone Age Assessment.

Chaitanya Mehta, Bibi Ayeesha, Ayesha Sotakanal

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an automated deep learning method for bone age assessment using hand X-rays. The AI system achieved a high accuracy, offering a valuable tool for predicting skeletal age in children.

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    Area of Science:

    • Pediatric Radiology
    • Artificial Intelligence in Medicine
    • Medical Imaging Analysis

    Background:

    • Bone age assessment is crucial for identifying endocrine and metabolic disorders in children.
    • Skeletal age provides a more accurate measure of biological growth than chronological age.
    • Current methods often rely on manual interpretation of hand X-rays, which can be subjective.

    Purpose of the Study:

    • To develop a fully automated deep learning approach for bone age assessment.
    • To improve the accuracy and consistency of bone age predictions.
    • To provide an AI-based supplementary tool for clinical decision-making.

    Main Methods:

    • Utilized a dataset from the 2017 Pediatric Bone Age Challenge.
    • Employed transfer learning with the pre-trained InceptionV3 neural network architecture.
    • Trained the model on left hand X-ray images tagged with patient age and gender.

    Main Results:

    • The automated deep learning model achieved a mean absolute error of 5.921 months in bone age prediction.
    • Demonstrated high agreement among automated methods for evaluating X-rays.
    • The InceptionV3 architecture proved effective for this task.

    Conclusions:

    • A fully automated deep learning system can accurately predict bone age from hand X-rays.
    • This AI-driven approach can serve as an effective assistive tool for clinicians.
    • The method shows promise for enhancing the diagnosis of developmental abnormalities.