用MRI和深度学习量化脊柱老化程度，可辅助评估脊柱健康。
An Artificial Intelligence Framework for Measuring Human Spine Aging Using MRI
- 基于1.8万例MRI数据，用深度学习预测脊柱年龄。
- 脊柱年龄差与椎间盘突出、骨赘等退变相关，具有临床意义。
- 适合关注脊柱健康或医学影像分析的研究者参考。
人类脊柱由33块椎骨组成，是维持身体结构与健康生活的关键。随着年龄增长，脊柱易出现退行性病变，可通过磁共振成像（MRI）识别。本文提出一种基于计算机视觉的深度学习方法，利用超过18,000例MRI序列图像估计脊柱年龄。数据仅包含仅由年龄相关退变引起的病例。通过统一流形近似与投影（UMAP）和基于密度的聚类（HDBSCAN）识别常见退变模式，建立入选标准。模型选择通过详尽的消融实验确定，涵盖数据量、损失函数及不同脊柱区域的影响。通过计算实际年龄与模型预测年龄之差（脊柱年龄差，SAG），评估模型临床价值，并分析其与椎间盘膨出、骨赘、脊柱狭窄、骨折以及吸烟、体力劳动等生活方式因素的关联。结果表明，SAG与多种退变疾病及生活因素显著相关，可能作为衡量整体脊柱健康的潜在生物标志物。
原文摘要 · Abstract (English)
The human spine is a complex structure composed of 33 vertebrae. It holds the body and is important for leading a healthy life. The spine is vulnerable to age-related degenerations that can be identified through magnetic resonance imaging (MRI). In this paper we propose a novel computer-vison-based deep learning method to estimate spine age using images from over 18,000 MRI series. Data are restricted to subjects with only age-related spine degeneration. Eligibility criteria are created by identifying common age-based clusters of degenerative spine conditions using uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN). Model selection is determined using a detailed ablation study on data size, loss, and the effect of different spine regions. We evaluate the clinical utility of our model by calculating the difference between actual spine age and model-predicted age, the spine age gap (SAG), and examining the association between these differences and spine degenerative conditions and lifestyle factors. We find that SAG is associated with conditions including disc bulges, disc osteophytes, spinal stenosis, and fractures, as well as lifestyle factors like smoking and physically demanding work, and thus may be a useful biomarker for measuring overall spine health.
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