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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.
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Updated: May 3, 2026

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

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DC-YOLOv5-based target detection algorithm for cervical vertebral maturation.

Man Jiang^1,2,3, Yun Hu^1,2,3, Jianxia Li^1,2,3

¹Department of Stomatological Hospital, Chongqing Medical University, No.426 Songshibei Road, Yubei District, Chongqing, 401147, China.

Physical and Engineering Sciences in Medicine

|August 12, 2024

Summary

This summary is machine-generated.

A new DC-YOLOv5 model automates cervical vertebral maturation (CVM) staging from radiographs. This AI approach enhances accuracy and speed for orthodontic and orthopedic treatment planning.

Keywords:

Bone age Cephalometric radiographs Cervical vertebral maturation assessment Target detection YOLOv5

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Precision Measurements and Parametric Models of Vertebral Endplates

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Published on: September 17, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Orthodontics

Background:

Cervical vertebral maturation (CVM) is crucial for timing orthodontic and orthopedic treatments.
Accurate CVM staging aids in effective treatment planning.
Current methods may lack full automation and optimal accuracy.

Purpose of the Study:

To develop a fully automated target detection model for CVM staging.
To enhance the accuracy and efficiency of CVM identification using deep learning.
To introduce an optimized YOLOv5 model for analyzing cephalometric radiographs.

Main Methods:

A dataset of 1800 labeled cephalometric radiographs was used.
The DC-YOLOv5 model was developed, optimizing YOLOv5 with Wise-IOU and Res-dcn-head.
Convolutional Block Attention Module (CBAM) was integrated for improved feature focus.
Performance was evaluated using F1 scores, mAP, precision, and recall.

Main Results:

DC-YOLOv5 achieved high performance metrics: F1 score of 0.993, mAP0.5 of 0.994, and mAP0.5:0.95 of 0.943.
The model demonstrated faster convergence, superior accuracy, and greater robustness compared to four other models.
Gradient Class Activation Mapping (CAM) was used to analyze prediction regions.

Conclusions:

The DC-YOLOv5 algorithm provides highly accurate and robust CVM identification.
This automated approach supports faster and more precise clinical diagnosis in orthodontics.
The model contributes positively to advancements in medical imaging and clinical decision-making.