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.

翻译：近年来，人工智能（AI）已广泛应用于医学领域，尤其在医学影像分析方面，这得益于计算机视觉与深度学习方法的进步。这对克服骨转移（BM）这类常见且复杂的骨骼恶性肿瘤带来的挑战尤为重要。事实上，开发机器学习（ML）技术用于骨转移分析的肿瘤影像学研究日益增多。为全面概述当前利用人工智能进行骨转移分析的最新技术与进展，本综述遵循PRISMA指南开展研究。首先，本文从临床与肿瘤学角度阐释了骨转移及其所采用的医学影像模态，并讨论了各自的优势与局限性。随后，综述聚焦于现代方法，涵盖骨转移分析的主要任务：分类、检测与分割。结果分析表明，机器学习技术可在骨转移分析中取得优异性能，并具有显著潜力以提高临床医生效率、应对时间与成本限制。此外，仍需进一步研究验证机器学习工具的临床效能，并推动其融入常规临床实践。