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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...

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Related Experiment Video

Updated: Oct 29, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

CT Cervical Spine Fracture Detection Using a Convolutional Neural Network.

J E Small¹, P Osler², A B Paul³

¹From the Departments of Neuroradiology (J.E.S., A.B.P., M.K.) Juan.E.Small@Lahey.org.

AJNR. American Journal of Neuroradiology

|July 13, 2021

Summary

This summary is machine-generated.

A convolutional neural network shows promise for detecting cervical spine fractures on CT scans, assisting radiologists. Further improvements in sensitivity are needed for optimal clinical integration.

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Last Updated: Oct 29, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Multidetector CT is the standard for evaluating cervical spine trauma.
Convolutional neural networks (CNNs) are being developed for medical image analysis.

Purpose of the Study:

To evaluate the performance of an FDA-approved CNN (C-spine) in detecting cervical spine fractures on CT.
To compare the diagnostic accuracy of the CNN with radiologist performance.

Main Methods:

Analyzed 665 CT examinations using the C-spine CNN.
Established ground truth by reviewing CT, MR imaging, and CNN output.
Calculated sensitivity, specificity, accuracy, and predictive values.

Main Results:

CNN accuracy was 92% (90%-94%), sensitivity 76% (68%-83%), specificity 97% (95%-98%).
Radiologist accuracy was 95% (94%-97%), sensitivity 93% (88%-97%), specificity 96% (94%-98%).
Missed fractures by CNN and radiologists were similar, including anterior osteophytes and lower cervical spine fractures.

Conclusions:

The CNN shows potential for worklist prioritization and assisting radiologists in cervical spine fracture detection.
Understanding CNN limitations is crucial for clinical integration.
Enhanced CNN sensitivity would improve diagnostic utility.