Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges

TL;DR

This review discusses how artificial intelligence, especially machine learning, is advancing the analysis of bone metastases in medical imaging, highlighting current methods, challenges, and future opportunities.

Contribution

It provides a comprehensive overview of AI applications in bone metastasis analysis, emphasizing recent advancements, clinical perspectives, and the need for further validation.

Findings

ML achieves promising performance in BM analysis

AI can improve clinician efficiency and reduce costs

Further research needed for clinical validation

Abstract

In recent years, Artificial Intelligence (AI) has been widely used in medicine, particularly in the analysis of medical imaging, which has been driven by advances in computer vision and deep learning methods. This is particularly important in overcoming the challenges posed by diseases such as Bone Metastases (BM), a common and complex malignancy of the bones. Indeed, there have been an increasing interest in developing Machine Learning (ML) techniques into oncologic imaging for BM analysis. In order to provide a comprehensive overview of the current state-of-the-art and advancements for BM analysis using artificial intelligence, this review is conducted with the accordance with PRISMA guidelines. Firstly, this review highlights the clinical and oncologic perspectives of BM and the used medical imaging modalities, with discussing their advantages and limitations. Then the review focuses on modern approaches with considering the main BM analysis tasks, which includes: classification, detection and segmentation. The results analysis show that ML technologies can achieve promising performance for BM analysis and have significant potential to improve clinician efficiency and cope with time and cost limitations. Furthermore, there are requirements for further research to validate the clinical performance of ML tools and facilitate their integration into routine clinical practice.