Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China; Jinan University, Guangzhou, Guangdong, PR China.
Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Guangzhou, PR China.
Bone. 2020 Nov;140:115561. doi: 10.1016/j.bone.2020.115561. Epub 2020 Jul 28.
Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lumbar spine X-ray images. Herein, we developed the DCNN models based on the training dataset, which comprising 1616 lumbar spine X-ray images from 808 postmenopausal women (aged 50 to 92 years). DXA-derived bone mineral density (BMD) measures were used as the reference standard. We categorized patients into three groups according to DXA BMD T-score: normal (T ≥ -1.0), osteopenia (-2.5 < T < -1.0), and osteoporosis (T ≤ -2.5). T-scores were calculated by using the BMD dataset of young Chinese female aged 20-40 years as a reference. A 3-class DCNN model was trained to classify normal BMD, osteoporosis, and osteopenia. Model performance was tested in a validation dataset (204 images from 102 patients) and two test datasets (396 images from 198 patients and 348 images from 147 patients respectively). Model performance was assessed by the receiver operating characteristic (ROC) curve analysis. The results showed that in the test dataset 1, the model diagnosing osteoporosis achieved an AUC of 0.767 (95% confidence interval [CI]: 0.701-0.824) with sensitivity of 73.7% (95% CI: 62.3-83.1), the model diagnosing osteopenia achieved an AUC of 0.787 (95% CI: 0.723-0.842) with sensitivity of 81.8% (95% CI: 67.3-91.8); In the test dataset 2, the model diagnosing osteoporosis yielded an AUC of 0.726 (95% CI: 0.646-0.796) with sensitivity of 68.4% (95% CI: 54.8-80.1), the model diagnosing osteopenia yielded an AUC of 0.810 (95% CI, 0.737-0.870) with sensitivity of 85.3% (95% CI, 68.9-95.0). Accordingly, a deep learning diagnostic network may have the potential in screening osteoporosis and osteopenia based on lumbar spine radiographs. However, further studies are necessary to verify and improve the diagnostic performance of DCNN models.
骨质疏松症是一种普遍但未被充分诊断的疾病。与双能 X 射线吸收法（DXA）测量相比，我们旨在开发一种基于腰椎 X 射线图像的深度学习卷积神经网络（DCNN）模型来分类骨量减少和骨质疏松症。在此，我们基于包含 808 名绝经后女性（年龄 50 至 92 岁）的 1616 张腰椎 X 射线图像的训练数据集开发了 DCNN 模型。DXA 得出的骨密度（BMD）测量值被用作参考标准。我们根据 DXA BMD T 评分将患者分为三组：正常（T≥-1.0）、骨量减少（-2.5<T<-1.0）和骨质疏松症（T≤-2.5）。T 评分是使用 20-40 岁年轻中国女性的 BMD 数据集作为参考计算得出的。我们训练了一个 3 类 DCNN 模型来分类正常 BMD、骨质疏松症和骨量减少。模型性能在验证数据集（204 张来自 102 名患者的图像）和两个测试数据集（396 张来自 198 名患者的图像和 348 张来自 147 名患者的图像）中进行了测试。通过接收者操作特征（ROC）曲线分析评估模型性能。结果表明，在测试数据集 1 中，诊断骨质疏松症的模型获得了 0.767 的 AUC（95%置信区间[CI]：0.701-0.824），敏感性为 73.7%（95%CI：62.3-83.1），诊断骨量减少的模型获得了 0.787 的 AUC（95%CI：0.723-0.842），敏感性为 81.8%（95%CI：67.3-91.8）；在测试数据集 2 中，诊断骨质疏松症的模型产生了 0.726 的 AUC（95%CI：0.646-0.796），敏感性为 68.4%（95%CI：54.8-80.1），诊断骨量减少的模型产生了 0.810 的 AUC（95%CI，0.737-0.870），敏感性为 85.3%（95%CI，68.9-95.0）。因此，深度学习诊断网络可能具有基于腰椎 X 射线筛查骨质疏松症和骨量减少的潜力。然而，需要进一步的研究来验证和提高 DCNN 模型的诊断性能。
