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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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Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.

N van Nistelrooij^1,2, S Schitter³, P van Lierop¹

¹Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.

Journal of Dental Research

|June 24, 2024

Summary

This summary is machine-generated.

JawFracNet, an AI tool, accurately detects mandibular fractures in 3D scans. This method aids in precise identification of facial skeleton injuries, improving patient management.

Keywords:

artificial intelligence cone-beam computed tomography deep learning mandibular fractures maxillofacial surgery open source software

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Area of Science:

Oral and Maxillofacial Surgery
Medical Imaging
Artificial Intelligence in Medicine

Background:

Mandibular fractures are common facial skeleton injuries, second only to nasal bone fractures.
Accurate identification of fracture locations is crucial for effective clinical management.
Existing methods for mandibular fracture detection in cone-beam computed tomography (CBCT) scans can be time-consuming and require specialized expertise.

Purpose of the Study:

To develop and evaluate JawFracNet, an automated artificial intelligence (AI) method for detecting mandibular fractures in CBCT scans.
To establish a benchmark for mandibular fracture detection using AI in medical imaging.
To provide a publicly accessible tool and code for research and clinical application.

Main Methods:

JawFracNet utilizes a 3-stage neural network model processing 3D patches from CBCT scans.
Stage 1: Mandible segmentation. Stage 2: Fracture segmentation. Stage 3: Fracture classification within a patch.
Final fracture segmentation is achieved by aggregating voxel-level and patch-level predictions.

Main Results:

The study included 164 CBCT scans without fractures and 171 scans with fractures.
JawFracNet achieved a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures.
The AI method demonstrated high accuracy in identifying and segmenting mandibular fractures.

Conclusions:

JawFracNet represents a significant advancement in the automated detection of mandibular fractures.
The developed AI method provides a reliable and accurate tool for analyzing CBCT scans.
Public availability of the code and tool promotes further research and clinical adoption.