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Classification of Bones

Classification of Bones

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 long...

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Related Experiment Video

Updated: Jul 9, 2026

A Rat Tibial Growth Plate Injury Model to Characterize Repair Mechanisms and Evaluate Growth Plate Regeneration Strategies

A Rat Tibial Growth Plate Injury Model to Characterize Repair Mechanisms and Evaluate Growth Plate Regeneration Strategies

Published on: July 4, 2017

Deep learning for tibial plateau fracture detection and classification.

N van der Gaast¹, P Bagave², N Assink³

¹Department of Orthopaedics & Trauma Surgery, Flinders Medical Centre and Flinders University, Adelaide, SA, Australia; Department of Trauma Surgery, Radboud University Medical Center, Radboud University Nijmegen, the Netherlands.

|March 2, 2025

Summary

Deep learning models can detect tibia plateau fractures with high sensitivity but struggle with accurate Schatzker classification. Further research is needed to improve classification accuracy for these complex fractures.

Keywords:

Artificial intelligence Deep learning Tibial plateau fractures

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Area of Science:

Orthopedic surgery
Radiology
Artificial intelligence

Background:

Deep learning (DL) shows promise in interpreting radiographs for fracture detection and classification.
No prior studies have developed a computer vision model for tibia plateau fractures using the Schatzker classification system.

Purpose of the Study:

To develop a deep learning model for detecting tibia plateau fractures.
To classify tibia plateau fractures according to the Schatzker classification system.

Main Methods:

A multicenter dataset of 1506 knee radiographs from 753 patients was collected and annotated.
Two convolutional neural networks, GoogleNet and ResNet, were trained for fracture detection and Schatzker classification.

Main Results:

The GoogleNet model achieved high sensitivity (92.7%) for detecting tibia plateau fractures, with moderate accuracy (70.4%).
Classification accuracy for the Schatzker system was limited (34.6% accuracy, 32.1% sensitivity).

Conclusions:

Deep learning algorithms can effectively detect tibia plateau fractures.
Enhancing DL model accuracy for Schatzker classification requires further development, potentially exploring alternative classification systems due to low interobserver agreement.