论文标题
使用深度学习预测结构MRI的膝关节骨关节炎
Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning
论文作者
论文摘要
从结构MRI中的膝关节骨关节炎（KOA）进展的准确预测具有增强疾病理解和支持临床试验的潜力。先前的艺术专注于手动设计的成像生物标志物，这可能无法完全利用MRI扫描中存在的所有与疾病相关的信息。相比之下，我们的方法使用深度学习从原始数据端到端学习相关表示形式，并将其用于进步预测。该方法采用2D CNN来处理数据切片，并使用变压器汇总提取的功能。在大型队列上进行评估（n = 4,866），该方法的表现优于常规2D和3D CNN的型号，并达到平均精度为$ 0.58 \ pm0.03 $和ROC AUC，ROC AUC $ 0.78 \ pm0.01 $。本文对结构MRI的端到端KOA进展预测设定了基线。我们的代码可在https://github.com/mipt-oulu/oaprogressionmr上公开获取。
Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on manually designed imaging biomarkers, which may not fully exploit all disease-related information present in MRI scan. In contrast, our method learns relevant representations from raw data end-to-end using Deep Learning, and uses them for progression prediction. The method employs a 2D CNN to process the data slice-wise and aggregate the extracted features using a Transformer. Evaluated on a large cohort (n=4,866), the proposed method outperforms conventional 2D and 3D CNN-based models and achieves average precision of $0.58\pm0.03$ and ROC AUC of $0.78\pm0.01$. This paper sets a baseline on end-to-end KOA progression prediction from structural MRI. Our code is publicly available at https://github.com/MIPT-Oulu/OAProgressionMR.
