AI in Osteoporosis

TL;DR

This paper reviews AI methods for osteoporosis diagnosis, focusing on texture analysis, sparse representations, and deep learning, demonstrating their potential as clinical diagnostic tools.

Contribution

It introduces integrative sparse analysis techniques and compares various AI methods for bone texture classification and osteoporosis detection.

Findings

Deep neural networks improve classification accuracy.

Sparse representations enhance pattern recognition.

AI methods show promise for clinical osteoporosis diagnosis.

Abstract

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.