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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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Related Experiment Video

Updated: Jul 12, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Skeletal Fracture Detection with Deep Learning: A Comprehensive Review.

Zhihao Su¹, Afzan Adam¹, Mohammad Faidzul Nasrudin¹

¹Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Diagnostics (Basel, Switzerland)

|October 28, 2023

Summary

This summary is machine-generated.

This review clarifies deep learning tasks for bone fracture diagnosis from X-rays. It analyzes 40 papers, defining recognition, classification, detection, and localization for better AI development and clinical trust.

Keywords:

SDG4 X-ray images bone fracture detection deep learning algorithms

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Deep learning shows potential for diagnosing bone fractures from X-rays.
Current research faces challenges due to unclear task definitions and lack of explainability.
Existing reviews often lack technical depth or scope.

Purpose of the Study:

To establish precise definitions for deep learning tasks in bone fracture diagnosis (recognition, classification, detection, localization).
To analyze and summarize recent research methodologies, datasets, and outcomes.
To identify key areas for future research in AI-driven fracture diagnosis.

Main Methods:

Comprehensive literature review of 337 papers from WOS, Scopus, and EI.
In-depth analysis and evaluation of 40 selected recent studies.
Development of a generalized processing framework for deep learning in fracture diagnosis.

Main Results:

Clear definitions provided for bone fracture recognition, classification, detection, and localization tasks.
Summaries of 40 studies detailing bones, objectives, datasets, methods, and results.
Identification of critical future research directions, including interpretability and multimodal data integration.

Conclusions:

This review addresses the need for standardized task definitions in deep learning for fracture diagnosis.
Findings provide a foundation for advancing AI in radiology with improved interpretability and clinical decision support.
Future work should focus on explainable AI, multimodal data, and therapeutic recommendations.