Department of Radiology.
Division of Rheumatology, Department of Medicine, New York University School of Medicine, USA.
Curr Opin Rheumatol. 2019 Jul;31(4):368-375. doi: 10.1097/BOR.0000000000000607.
Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and deep learning models have found a role in osteoporosis, both to model the risk of fragility fracture, and to help with the identification and segmentation of images.
Here we survey the latest research in the artificial intelligence application to the prediction of osteoporosis that has been published between January 2017 and March 2019. Around half of the articles that are covered here predict (by classification or regression) an indicator of osteoporosis, such as bone mass or fragility fractures; the other half of studies use tools for automatic segmentation of the images of patients with or at risk of osteoporosis. The data for these studies include diverse signal sources: acoustics, MRI, CT, and of course, X-rays.
New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value. Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement, such as accounting for positive/negative class bias. We urge care when reporting accuracy metrics, and when comparing such metrics between different studies.
人工智能工具在医学诊断中找到了新的应用。这些工具具有捕捉潜在趋势和模式的潜力，而这是以前的建模能力无法实现的。机器学习和深度学习模型在骨质疏松症中找到了应用，既能预测脆性骨折的风险，也有助于识别和分割图像。
本文调查了 2017 年 1 月至 2019 年 3 月期间发表的人工智能在骨质疏松症预测中的最新应用研究。本文涵盖的研究中，有一半左右的文章通过分类或回归预测骨质疏松症的一个指标，如骨量或脆性骨折；另一半研究则使用工具自动分割有或有骨质疏松症风险的患者的图像。这些研究的数据包括多种信号源：声学、MRI、CT，当然还有 X 射线。
自动图像分割和骨折风险预测的新方法显示出有前景的临床价值。尽管这些新的发展在骨质疏松症研究中取得了成功的初步应用，但它们的发展仍在不断改进，例如考虑阳性/阴性分类偏差。我们敦促在报告准确性指标时要谨慎，并在不同研究之间比较此类指标时要谨慎。
