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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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Segmentation of Cemento-Osseous Dysplasias Using an Artificial Intelligence Algorithm.

Duygu Çelik Özen¹, Oğuzhan Altun¹, Şuayip Burak Duman²

¹Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey.

International Dental Journal

|January 4, 2026

Summary

Artificial intelligence (AI) successfully segmented cemento-osseous lesions in cone beam computed tomography (CBCT) images. This automated approach shows promise for improving the diagnosis and follow-up of cemento-osseous dysplasias.

Keywords:

Artificial Intelligence Cemento-osseous dysplasia Cone beam computed tomography Deep learning

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Area of Science:

Radiology
Oral and Maxillofacial Radiology
Medical Imaging
Artificial Intelligence

Background:

Cemento-osseous dysplasias are complex bone pathologies requiring accurate diagnosis.
Artificial intelligence (AI) is increasingly utilized in medical imaging for analyzing pathologies.
Accurate segmentation of lesions is crucial for diagnosis and follow-up.

Purpose of the Study:

To segment cemento-osseous lesions using AI algorithms on cone beam computed tomography (CBCT) images.
To evaluate the diagnostic performance of an AI model for cemento-osseous dysplasia diagnosis.

Main Methods:

Retrospective review of CBCT images diagnosed with cemento-osseous dysplasias.
Automated segmentation of lesions using an nnU-Net v2-based algorithm in Python.
Training, validation, and testing split of 80%, 10%, and 10% of the data.

Main Results:

The AI model achieved high performance in segmenting cemento-osseous dysplasias.
Key metrics included precision (0.805), sensitivity (0.889), Dice Coefficient (0.839), and Jaccard Index (0.730).

Conclusions:

The AI model demonstrated successful segmentation of cemento-osseous dysplasias.
Automated segmentation offers potential for precise lesion definition and standardized follow-up.
These findings provide guidance for physicians in diagnosing cemento-osseous dysplasias.