Background:Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis.Objectives:This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs.Design:This was a single-center, retrospective study.Methods:An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo.Results:The proposed screening tool illustrated a better performance (sensitivity: 97.2%; specificity: 95.6%; positive predictive value: 95.7%; negative predictive value: 97.1%; area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA ( r = 0.996, p < 0.001).Conclusion:The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk.Trial registration:TMU-JIRB N201909036.

中文翻译：

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通过深度学习算法使用髋部 X 光照片进行自动骨质疏松症分类和 T 分数预测


背景：尽管双能 X 射线吸收测定法 (DXA) 是诊断骨质疏松症的金标准，但它是一种未充分利用的骨质疏松症筛查工具。 目标：本研究提出并验证了卷积神经网络 (CNN) 模型的可控特征层一种预处理图像算法，用于通过简单的髋部 X 光照片对骨质疏松症进行分类并预测近端髋部区域的 T 分数。设计：这是一项单中心回顾性研究。方法：来自 1730 名患者（年龄⩾）的 3460 张单侧髋部图像的图像数据集回顾性收集了 50 年来的数据，并匹配 DXA 评估对目标近端髋部区域的 T 分数进行训练（来自 1430 名患者的 2473 张单侧髋部图像）和测试（来自 300 名患者的 497 张单侧髋部图像）所提出的 CNN 模型。所有图像均使用全自动 CNN 模型 X1AI-Osteo 进行处理。结果：所提出的筛查工具显示出更好的性能（灵敏度：97.2%；特异性：95.6%；阳性预测值：95.7%；阴性预测值：97.1%；曲线下面积：0.96）优于开源 CNN 模型在预测骨质疏松症方面的效果。此外，当将年龄、体重指数和性别等变量组合为训练指标中的特征时，所提出的 CNN 模型与 DXA 之间目标臀部区域的 T 分数具有高度一致性（r = 0.996，p < 0.001）。结论：所提出的 CNN 模型可以通过简单的髋部 X 光片以高精度识别骨质疏松症并预测目标髋部区域的 T 分数，突出了基于人群的机会性骨质疏松症筛查的未来应用，成本低且适应性强更广泛的高危人群。试验注册：TMU-JIRB N201909036。