AI in Osteoporosis

09/22/2021
by   Sokratis Makrogiannis, et al.
0

In this chapter we explore and evaluate methods for trabecular bone characterization and osteoporosis diagnosis with increased interest in sparse approximations. We first describe texture representation and classification techniques, patch-based methods such as Bag of Keypoints, and more recent deep neural networks. Then we introduce the concept of sparse representations for pattern recognition and we detail integrative sparse analysis methods and classifier decision fusion methods. We report cross-validation results on osteoporosis datasets of bone radiographs and compare the results produced by the different categories of methods. We conclude that advances in the AI and machine learning fields have enabled the development of methods that can be used as diagnostic tools in clinical settings.

READ FULL TEXT

page 3

page 6

research
01/31/2018

A Survey of Recent Advances in Texture Representation

Texture is a fundamental characteristic of many types of images, and tex...
research
06/21/2014

An Open Source Pattern Recognition Toolbox for MATLAB

Pattern recognition and machine learning are becoming integral parts of ...
research
09/25/2018

Deep Neural Networks for Pattern Recognition

In the field of pattern recognition research, the method of using deep n...
research
06/26/2017

Multi-level SVM Based CAD Tool for Classifying Structural MRIs

The revolutionary developments in the field of supervised machine learni...
research
07/26/2021

Systematic Literature Review of Validation Methods for AI Systems

Context: Artificial intelligence (AI) has made its way into everyday act...
research
05/30/2022

AI-enabled Sound Pattern Recognition on Asthma Medication Adherence: Evaluation with the RDA Benchmark Suite

Asthma is a common, usually long-term respiratory disease with negative ...
research
06/02/2020

Robust multivariate methods in Chemometrics

This chapter presents an introduction to robust statistics with applicat...

Please sign up or login with your details

Forgot password? Click here to reset