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用多式变压器对膝关节骨关节炎进展的端到端预测
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
预测膝关节骨关节炎 (KOA) 的进展是具有挑战性的. 使用结构性MRI的深度学习模型显示出有希望的结果,匹配多式联络方法用于长期KOA预测,而多式联络数据在短期预测方面表现出色.
科学领域:
- 生物医学成像学 生物医学成像学
- 医学中的人工智能
- 肌肉骨研究的研究.
背景情况:
- 膝关节关节炎 (KOA) 是一种广泛的慢性疾病,没有治愈方法.
- 预测KOA的进展是复杂的,因为它的异质性质.
- 深度学习 (DL) 和多模式数据显示了KOA进展预测的潜力.
研究的目的:
- 用多式成像数据评估DL模型来预测KOA进展.
- 评估各种时间范围 (1-8年) 的预测性能.
- 为了确定从基于图像的预测中受益最多的子组.
主要方法:
- 使用基于变压器的DL模型来融合多模式膝盖成像数据 (MRI,X射线).
- 分析了来自骨关节炎倡议的大量数据集 (n=3967/2421).
- 使用ROC AUC和平均精度指标评估模型性能.
主要成果:
- 仅靠结构性膝盖MRI就能够在2-8年内竞争性预测放射性KOA进展 (ROC AUC0.70-0.76).
- 多模式成像 (X射线,结构性,组合性MRI) 改善了1年的KOA进展预测 (ROC AUC 0.76).
- 在创伤后膝关节关节炎病例中,预测准确度显著更高.
结论:
- 结构性膝盖MRI是预测长期KOA进展的强大工具.
- 多模式成像增强了短期KOA进展预测.
- 一个统一的,数据驱动的框架有助于KOA进展研究和临床试验设计.
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