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

• URL: http://arxiv.org/abs/2404.19598v1
• Date: Tue, 30 Apr 2024 14:49:03 GMT
• Title: Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges
• Authors: Marwa Afnouch, Fares Bougourzi, Olfa Gaddour, Fadi Dornaika, Abdelmalik Taleb-Ahmed,
• Abstract summary: This review highlights the current state-of-the-art and advancements for Bone Metastases analysis using artificial intelligence. ML technologies can achieve promising performance for BM analysis and have significant potential to improve clinician efficiency and cope with time and cost limitations.
• Score: 15.765725731972983
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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