发表机构
Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出用Transformer模型预测青少年特发性脊柱侧弯在9至24岁间的时序进展，基于合成数据训练并微调于真实DXA扫描数据，可准确预测脊柱曲线进展。
AI 中文摘要
青少年特发性脊柱侧弯被定义为在青春期发生的、原因不明的脊柱侧向弯曲。该疾病可导致显著的疼痛和残疾，且常在青春期快速进展。本文的目标是预测该疾病在9至24岁年龄段的时序进展，该进展通过一系列双能X射线吸收测定法（DXA）扫描测量。为此，我们训练了一个Transformer模型，该模型以脊柱曲线为输入，预测曲线进展。模型使用大规模合成的脊柱曲线及其时间序列数据集进行训练，涵盖不同的曲线类型和不同的进展模式。我们通过在包含多个时间点的真实DXA扫描数据集上评估模型，证明了模型能够从合成数据泛化到真实数据。我们发现，在真实数据上对模型进行微调能显著提升性能。该模型能够准确预测脊柱侧弯和正常病例中的脊柱曲线进展。
英文摘要
Adolescent Idiopathic Scoliosis is defined as a lateral curvature of the spine that develops during adolescence, without known cause. The condition can result in significant pain and disability, and often progresses rapidly during adolescence. The objective of this paper is to predict the progression of the condition in a temporal sequence from ages 9 to 24, as measured from a sequence of Dual X-ray Absorptiometry (DXA) scans. To this end, we train a transformer model that takes in the curve of the spine to predict curve progression. The model is trained using a large-scale synthetic dataset of spine curves and their time series, covering different curve types and different progression patterns. We show that the model is able to generalise from synthetic to real data by evaluating it on a dataset of real DXA scans covering multiple time points. We find that fine-tuning the model on real data gives a significant boost to performance. The model is able to accurately predict spine curve progression in both scoliosis and normal cases.
CommentsPublished in MICCAI ShapeMI 2026 Workshop
