Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Bones01:18

Classification of Bones

13.3K
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...
13.3K
Fractures: Bone Repair01:27

Fractures: Bone Repair

6.6K
Treatment for a fracture is based on the type of break, the bone affected, and the patient's age.
Minor fractures with no bone displacement are treated by immobilizing the fractured bone using a cast or splint. However, in the case of fractures with displaced bones, the broken bones are repositioned before immobilization to ensure successful healing without deformation and loss of function. The realignment of fractured bone ends is performed through a process called reduction. If the...
6.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Classification of pulmonary nodules by using hybrid features.

Computational and mathematical methods in medicine·2013
Same author

Mammographic mass detection using wavelets as input to neural networks.

Journal of medical systems·2010
Same author

Diagnosis of renal failure disease using Adaptive Neuro-Fuzzy Inference System.

Journal of medical systems·2010
Same author

Classification of the colonic polyps in CT-colonography using region covariance as descriptor features of suspicious regions.

Journal of medical systems·2010
Same author

Colonic polyp detection in CT colonography with fuzzy rule based 3D template matching.

Journal of medical systems·2009
Same author

Estimation of stream temperature in firtina creek (Rize-Turkiye) using artificial neural network model.

Journal of environmental biology·2007

Related Experiment Video

Updated: Mar 28, 2026

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

8.7K

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

Niyazi Kilic1, Erkan Hosgormez2

  • 1Engineering Faculty, Electrical and Electronics Department, Istanbul University, 34320, Avcilar, Istanbul, Turkey. niyazik@istanbul.edu.tr.

Journal of Medical Systems
|December 15, 2015
PubMed
Summary

This study shows ensemble learning accurately detects osteoporosis using bone densitometry. Machine learning models achieved 98.85% accuracy, predicting fractures from easily measured physical parameters.

Keywords:
Bone mineral densityEnsemble learning classificationIBkOsteoporosisRandom forestT-score

More Related Videos

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
09:02

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

1.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.7K

Related Experiment Videos

Last Updated: Mar 28, 2026

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model
07:12

Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

8.7K
Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
09:02

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

Published on: January 31, 2025

1.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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

Published on: August 16, 2020

7.7K

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Osteoporosis Research

Background:

  • Osteoporotic fractures pose a significant health risk.
  • Accurate detection of osteoporosis is crucial for fracture prevention.
  • Current diagnostic methods may have limitations.

Purpose of the Study:

  • To investigate the effectiveness of ensemble learning methods for osteoporotic fracture detection.
  • To evaluate the impact of physical bone densitometry parameters on classification accuracy.
  • To develop a non-invasive system for early warning of bone fractures.

Main Methods:

  • Six feature set models were created using various physical bone densitometry parameters.
  • Ensemble learning techniques including bagging, gradient boosting, and random subspace (RSM) were employed.
  • Instance-based learning (IBk) and random forest (RF) classifiers were applied to the feature sets.
  • Patients were classified into osteoporosis, osteopenia, and control groups.

Main Results:

  • The highest classification accuracy of 98.85% was achieved using a combination of five Bone Mineral Density (BMD) and five T-score values (model 6).
  • The Random Subspace Method (RSM) combined with the Random Forest (RF) classifier demonstrated superior performance.
  • Ensemble classifiers effectively distinguished between osteoporosis, osteopenia, and healthy individuals.

Conclusions:

  • Ensemble learning, particularly RSM-RF with specific bone densitometry parameters, shows high potential for accurate osteoporotic fracture detection.
  • The proposed system offers a non-invasive approach to identify at-risk patients, enabling early warnings before fractures occur.
  • Utilizing easily measurable physical parameters can significantly aid in the early diagnosis and management of osteoporosis.