Department of Radiology, Mayo Clinic, Jacksonville, Fl, 32224, USA.
Center for Augmented Intelligence, Mayo Clinic, Jacksonville, Fl, 32224, USA.
Stud Health Technol Inform. 2024 Aug 22;316:332-333. doi: 10.3233/SHTI240411.
Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged ≥ 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis/osteopenia.
患有低骨密度（BMD）的患者面临着骨折风险增加的问题，但往往未被诊断出来。因此，在对其他医学评估的患者进行机会性筛查低 BMD 至关重要。这项回顾性研究包括 422 名年龄≥50 岁的患者，他们在 12 个月内接受了来自三家不同供应商的双能 X 射线吸收法（DXA）和手部射线照相（数字 X 射线的方式）检查。数据集被随机分为训练/验证（n=338）和测试（n=84）数据集。我们试图预测骨质疏松症/骨量减少，并建立骨纹理分析与 DXA 测量之间的相关性。我们的研究结果表明，深度学习模型在检测骨质疏松症/骨量减少方面的准确率为 77.38%，灵敏度为 77.38%，特异性为 73.63%，曲线下面积（AUC）为 83%。这些发现表明，手部射线照相可以作为一种可行的筛选工具，用于识别需要进行正式 DXA 评估以确定骨质疏松症/骨量减少的个体。
