Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland.
Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland.
J Bone Miner Res. 2021 May;36(5):833-851. doi: 10.1002/jbmr.4292. Epub 2021 Apr 4.
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
骨质疏松症及其临床后果——骨折，是一种多因素疾病，已成为广泛研究的对象。机器学习 (ML) 的最新进展使人工智能 (AI) 领域能够在人类识别高维关系能力有限的复杂数据环境中取得令人瞩目的突破。骨质疏松症领域就是这样一个领域，尽管在应用 ML 方法方面存在技术和临床方面的担忧。本定性综述旨在概述其中的一些担忧，并为有兴趣应用 AI 改善骨质疏松症管理的利益相关者提供信息。在 PubMed 和 Web of Science 中进行系统检索，共纳入 89 项研究。这些研究涵盖了骨质疏松症管理的四个主要领域中的一个或多个方面：骨特性评估 (n = 13)、骨质疏松症分类 (n = 34)、骨折检测 (n = 32) 和风险预测 (n = 14)。通过 12 分检查表来确定报告和方法学质量。总的来说，这些研究的质量为中等，范围很广（模式得分为 6，范围为 2 到 11）。在许多研究中都发现了重大的局限性。未充分报告，尤其是在模型选择方面、数据分割不足以及外部验证比例低等问题是最常见的问题。然而，使用图像进行机会性骨质疏松症诊断或骨折检测已成为一种很有前途的方法，也是 ML 可以为骨质疏松症领域带来的主要贡献之一。开发基于 ML 的模型来识别新的骨折危险因素和提高骨折预测的努力是另外两个有前途的研究方向。一些研究还提供了关于基于模型的决策制定的潜在见解。最后，为了避免一些常见的陷阱，应该鼓励在开发和共享 ML 模型结果时使用标准化检查表。© 2021 美国骨矿研究协会 (ASBMR)。
