Nissinen Tomi, Suoranta Sanna, Saavalainen Taavi, Sund Reijo, Hurskainen Ossi, Rikkonen Toni, Kröger Heikki, Lähivaara Timo, Väänänen Sami P
Department of Applied Physics, University of Eastern Finland, POB1627, 70211 Kuopio, Finland.
Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland.
Bone Rep. 2021 Apr 24;14:101070. doi: 10.1016/j.bonr.2021.101070. eCollection 2021 Jun.
Dual-energy X-ray absorptiometry (DXA) is the gold standard imaging method for diagnosing osteoporosis in clinical practice. The DXA images are commonly used to estimate a numerical value for bone mineral density (BMD), which decreases in osteoporosis. Low BMD is a known risk factor for osteoporotic fractures. In this study, we used deep learning to identify lumbar scoliosis and structural abnormalities that potentially affect BMD but are often neglected in lumbar spine DXA analysis. In addition, we tested the approach's ability to predict fractures using only DXA images. A dataset of 2949 images gathered by Kuopio Osteoporosis Risk Factor and Prevention Study was used to train a convolutional neural network (CNN) for classification. The model was able to classify scoliosis with an AUC of 0.96 and structural abnormalities causing unreliable BMD measurement with an AUC of 0.91. It predicted fractures occurring within 5 years from the lumbar spine DXA scan with an AUC of 0.63, meeting the predictive performance of combined BMD measurements from the lumbar spine and hip. In an independent test set of 574 clinical patients, AUC for lumbar scoliosis was 0.93 and AUC for unreliable BMD measurements was 0.94. In each classification task, neural network visualizations indicated the model's predictive strategy. We conclude that deep learning could complement the well established DXA method for osteoporosis diagnostics by analyzing incidental findings and image reliability, and improve its predictive ability in the future.
双能X线吸收法（DXA）是临床实践中诊断骨质疏松症的金标准成像方法。DXA图像通常用于估计骨密度（BMD）的数值，骨质疏松症患者的骨密度会降低。低骨密度是已知的骨质疏松性骨折风险因素。在本研究中，我们使用深度学习来识别腰椎侧弯以及可能影响骨密度但在腰椎DXA分析中常被忽视的结构异常。此外，我们测试了该方法仅使用DXA图像预测骨折的能力。使用库奥皮奥骨质疏松症风险因素与预防研究收集的2949张图像数据集训练用于分类的卷积神经网络（CNN）。该模型能够以0.96的曲线下面积（AUC）对脊柱侧弯进行分类，以0.91的AUC对导致骨密度测量不可靠的结构异常进行分类。它预测腰椎DXA扫描后5年内发生的骨折的AUC为0.63，达到了腰椎和髋部联合骨密度测量的预测性能。在574名临床患者的独立测试集中，腰椎侧弯的AUC为0.93，骨密度测量不可靠的AUC为0.94。在每个分类任务中，神经网络可视化显示了模型的预测策略。我们得出结论，深度学习可以通过分析偶然发现和图像可靠性来补充成熟的DXA骨质疏松症诊断方法，并在未来提高其预测能力。
