Department of Urology, Emory University School of Medicine, Atlanta, GA, USA.
Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Eur Urol Focus. 2021 Jul;7(4):713-716. doi: 10.1016/j.euf.2021.03.013. Epub 2021 Mar 24.
Body composition analysis (BCA) generates objective anthropometric data that can inform prognostication and treatment decisions across a wide variety of urologic conditions. A patient's body composition, specifically muscle and adipose tissue mass, may be characterized via segmentation of cross-sectional images (computed tomography, magnetic resonance imaging) obtained as part of routine clinical care. Unfortunately, conventional semi-automated segmentation techniques are time- and resource-intensive, precluding translation into clinical practice. Machine learning (ML) offers the potential to automate and scale rapid and accurate BCA. To date, ML for BCA has relied on algorithms called convolutional neural networks designed to detect and analyze images in ways similar to human neuronal connections. This mini review provides a clinically oriented overview of ML and its use in BCA. We address current limitations and future directions for translating ML and BCA into clinical practice. PATIENT SUMMARY: Body composition analysis is the measurement of muscle and fat in your body based on analysis of computed tomography or magnetic resonance imaging scans. We discuss the use of machine learning to automate body composition analysis. The information provided can be used to guide shared decision-making and to help in identifying the best therapy option.
人体成分分析 (BCA) 可生成客观的人体测量学数据，可用于预测和治疗各种泌尿科疾病。患者的身体成分，特别是肌肉和脂肪组织的质量，可以通过对作为常规临床护理一部分获得的横截面图像（计算机断层扫描、磁共振成像）进行分割来描述。不幸的是，传统的半自动分割技术既耗时又耗费资源，无法转化为临床实践。机器学习 (ML) 提供了自动化和扩展快速准确 BCA 的潜力。迄今为止，BCA 的 ML 依赖于称为卷积神经网络的算法，这些算法旨在以类似于人类神经元连接的方式检测和分析图像。这篇迷你综述提供了一个面向临床的 ML 及其在 BCA 中的应用概述。我们讨论了将 ML 和 BCA 转化为临床实践的当前限制和未来方向。患者总结：人体成分分析是根据计算机断层扫描或磁共振成像扫描分析来测量体内肌肉和脂肪的方法。我们讨论了使用机器学习来自动进行人体成分分析。提供的信息可用于指导共同决策，并有助于确定最佳治疗方案。
