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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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Imaging of the Microstructural Failure Mechanism in the Human Hip

Imaging of the Microstructural Failure Mechanism in the Human Hip

Published on: September 29, 2023

Deep Learning Model for Differentiating Between Neoplastic Pathologic Fracture and Nonpathologic Fracture Using Hip

Shinn Kim^1,2, Kyoungseob Shin³, Han-Soo Kim^1,4

¹Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, Republic of Korea.

The Journal of Bone and Joint Surgery. American Volume

|November 26, 2025

Summary

This summary is machine-generated.

A new deep learning model accurately distinguishes neoplastic pathologic fractures from nonpathologic fractures on hip radiographs. This AI tool can aid surgeons in diagnosis, especially in resource-limited settings.

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Area of Science:

Orthopaedic Surgery
Medical Imaging
Artificial Intelligence

Background:

Radiographs are the primary imaging method for fractures.
Distinguishing neoplastic pathologic fractures from nonpathologic fractures on radiographs can be difficult.
Diagnostic accuracy is crucial for effective patient management.

Purpose of the Study:

To develop and evaluate a deep learning model for differentiating neoplastic pathologic fractures from nonpathologic fractures on hip radiographs.
To enhance diagnostic accuracy in fracture classification.
To provide a tool to assist clinicians in interpreting radiographic findings.

Main Methods:

A retrospective, multicenter study analyzed anteroposterior hip radiographs.
A deep learning model was trained and tested on data from 338 patients.
External validation was performed on 488 patients across three additional institutions.

Main Results:

The model achieved an accuracy of 0.880 internally and 0.848 externally.
Internal sensitivity was 0.882 and specificity was 0.879.
External validation showed a sensitivity of 0.910 and specificity of 0.786, comparable to orthopaedic surgeons.

Conclusions:

The deep learning model reliably distinguishes neoplastic pathologic fractures from nonpathologic fractures.
The model can support orthopaedic surgeons, particularly in resource-limited environments.
The tool has the potential to optimize radiographic interpretation and improve patient care.