Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
J Clin Endocrinol Metab. 2023 Mar 10;108(4):775-783. doi: 10.1210/clinem/dgac702.
Osteoporosis is a disease characterized by low bone mass and microarchitectural deterioration leading to increased bone fragility and fracture risk. Typically, osteoporotic fractures occur at the spine, hip, distal forearm, and proximal humerus, but other skeletal sites may be affected as well. One of the major challenges in the management of osteoporosis lies in the fact that although the operational diagnosis is based on bone mineral density (BMD) as measured by dual x-ray absorptiometry, the majority of fractures occur at nonosteoporotic BMD values. Furthermore, osteoporosis often remains undiagnosed regardless of the low severity of the underlying trauma. Also, there is only weak consensus among the major guidelines worldwide, when to treat, whom to treat, and which drug to use. Against this background, increasing efforts have been undertaken in the past few years by artificial intelligence (AI) developers to support and improve the management of this disease. The performance of many of these newly developed AI algorithms have been shown to be at least comparable to that of physician experts, or even superior. However, even if study results appear promising at a first glance, they should always be interpreted with caution. Use of inadequate reference standards or selection of variables that are of little or no value in clinical practice are limitations not infrequently found. Consequently, there is a clear need for high-quality clinical research in this field of AI. This could, eg, be achieved by establishing an internationally consented "best practice framework" that considers all relevant stakeholders.
骨质疏松症是一种以骨量低和微结构恶化为特征的疾病，导致骨脆性增加和骨折风险增加。通常，骨质疏松性骨折发生在脊柱、髋部、远端前臂和近端肱骨，但其他骨骼部位也可能受到影响。骨质疏松症管理的主要挑战之一在于，尽管操作性诊断是基于双能 X 线吸收法测量的骨密度（BMD），但大多数骨折发生在非骨质疏松性 BMD 值。此外，无论潜在创伤的严重程度如何，骨质疏松症通常仍未被诊断。此外，全球主要指南之间的共识也很薄弱，何时治疗、治疗谁以及使用哪种药物。在此背景下，过去几年人工智能（AI）开发者在支持和改善这种疾病的管理方面做出了越来越多的努力。许多新开发的 AI 算法的性能至少与医生专家相当，甚至更优。然而，即使研究结果乍一看很有希望，也应始终谨慎解释。经常会发现存在使用不适当的参考标准或选择在临床实践中几乎没有或没有价值的变量的局限性。因此，人工智能领域需要高质量的临床研究。这可以通过建立一个国际认可的“最佳实践框架”来实现，该框架考虑到所有相关利益攸关方。
