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使用OsteoSight™通过髋部和骨盆X光片对骨质疏松症进行机会性评估:一种基于人工智能的工具在美国人群中的验证

Opportunistic assessment of osteoporosis using hip and pelvic X-rays with OsteoSight™: validation of an AI-based tool in a US population.

作者信息

Pignolo Robert J, Connell John J, Briggs Will, Kelly Catherine J, Tromans Chris, Sultana Naima, Brady J Michael

机构信息

Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA.

Naitive Technologies Ltd, London, EC1N 2SW, UK.

出版信息

Osteoporos Int. 2025 Jun;36(6):1053-1060. doi: 10.1007/s00198-025-07487-0. Epub 2025 Apr 22.

Abstract

UNLABELLED

Identifying patients at risk of low bone mineral density (BMD) from X-rays presents an attractive approach to increase case finding. This paper showed the diagnostic accuracy, reproducibility, and robustness of a new technology: OsteoSight™. OsteoSight could increase diagnosis and preventive treatment rates for patients with low BMD.

PURPOSE

This study aimed to evaluate the diagnostic accuracy, reproducibility, and robustness of OsteoSight™, an automated image analysis tool designed to identify low bone mineral density (BMD) from routine hip and pelvic X-rays. Given the global rise in osteoporosis-related fractures and the limitations of current diagnostic paradigms, OsteoSight offers a scalable solution that integrates into existing clinical workflows.

METHODS

Performance of the technology was tested across three key areas: (1) diagnostic accuracy in identifying low BMD as compared to dual-energy X-ray absorptiometry (DXA), the clinical gold standard; (2) reproducibility, through analysis of two images from the same patient; and (3) robustness, by evaluating the tool's performance across different patient demographics and X-ray scanner hardware.

RESULTS

The diagnostic accuracy of OsteoSight for identifying patients at risk of low BMD was area under the receiver operating characteristic curve (AUROC) 0.834 [0.789-0.880], with consistent results across subgroups of clinical confounders and X-ray scanner hardware. Specificity 0.852 [0.783-0.930] and sensitivity 0.628 [0.538-0.743] met pre-specified acceptance criteria. The pre-processing pipeline successfully excluded unsuitable cases including incorrect body parts, metalwork, and unacceptable femur positioning.

CONCLUSION

The results demonstrate that OsteoSight is accurate in identifying patients with low BMD. This suggests its utility as an opportunistic assessment tool, especially in settings where DXA accessibility is limited or not recently performed. The tool's reproducibility and robust performance across various clinical confounders further supports its integration into routine orthopedic and medical practices, potentially broadening the reach of osteoporosis assessment and enabling earlier intervention for at-risk patients.

摘要

未标注

通过X射线识别骨密度(BMD)低风险患者是一种很有吸引力的增加病例发现的方法。本文展示了一种新技术——OsteoSight™的诊断准确性、可重复性和稳健性。OsteoSight可以提高骨密度低患者的诊断率和预防性治疗率。

目的

本研究旨在评估OsteoSight™的诊断准确性、可重复性和稳健性,这是一种旨在从常规髋部和骨盆X射线中识别低骨密度(BMD)的自动图像分析工具。鉴于全球骨质疏松相关骨折的增加以及当前诊断模式的局限性,OsteoSight提供了一种可扩展的解决方案,可融入现有的临床工作流程。

方法

该技术的性能在三个关键领域进行了测试:(1)与临床金标准双能X线吸收法(DXA)相比,识别低骨密度的诊断准确性;(2)通过分析同一患者的两张图像来评估可重复性;(3)通过评估该工具在不同患者人群和X射线扫描仪硬件上的性能来评估稳健性。

结果

OsteoSight识别低骨密度风险患者的诊断准确性为受试者操作特征曲线下面积(AUROC)0.834 [0.789 - 0.880],在临床混杂因素和X射线扫描仪硬件的亚组中结果一致。特异性为0.852 [0.783 - 0.930],敏感性为0.628 [0.538 - 0.743],均达到预先设定的接受标准。预处理流程成功排除了不合适的病例,包括身体部位不正确、有金属植入物以及股骨定位不可接受的情况。

结论

结果表明,OsteoSight在识别低骨密度患者方面是准确的。这表明它作为一种机会性评估工具的实用性,特别是在DXA可及性有限或近期未进行DXA检查的情况下。该工具在各种临床混杂因素中的可重复性和稳健性能进一步支持将其整合到常规骨科和医疗实践中,有可能扩大骨质疏松评估的范围,并为高危患者实现早期干预。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9978/12122585/8e2e40c1fea5/198_2025_7487_Fig1_HTML.jpg

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