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.
Abstract (translated)
近年来，人工智能（AI）在医学领域得到了广泛应用，特别是在医学影像分析方面，这得益于计算机视觉和深度学习技术的进步。这对于克服骨转移（BM）等疾病的挑战具有重要意义。事实上，越来越多地关注于开发用于BM分析的机器学习（ML）技术。为了全面回顾使用人工智能对BM分析的现状和进展，本文根据PRISMA指南进行撰写。首先，本文突出了BM的临床和病理观点以及所使用的医学影像手段，并讨论了它们的优缺点。接着，重点关注了考虑主要BM分析任务的现代方法，包括分类、检测和分割。结果分析表明，ML技术在BM分析方面可以实现良好的性能，具有显著提高临床效率和应对时间和成本限制的潜力。此外，还有必要进一步研究验证ML工具的临床性能，并促进它们融入临床实践。
URL
https://arxiv.org/abs/2404.19598
