基本事实
人工智能
分割
计算机科学
模式识别(心理学)
分类器(UML)
骨关节炎
深度学习
活动形状模型
形状分析(程序分析)
计算机视觉
医学
静态分析
病理
程序设计语言
替代医学
作者
Francesco Calivá,Sarthak Kamat,Alejandro Morales Martinez,Sharmila Majumdar,Valentina Pedoia
标识
DOI:10.1016/j.media.2022.102388
摘要
Bone shape changes are considered a relevant biomarker in understanding the onset and progression of knee osteoarthritis (OA). This study used a novel deep learning pipeline to predict longitudinal bone shape changes in the femur four years in advance, using bone surfaces that were extracted in knee MRIs from the OA initiative study, via a segmentation procedure and encoded as shape maps using spherical coordinates. Given a sequence of three consecutive shape maps (collected in a time window of 24 months), a fully convolutional network was trained to predict the whole bone surface 48 months after the last observed time point, and a classifier to diagnose OA in the predicted maps. For this, a novel multi-term loss function, based on contrastive learning was designed. Experimental results show that the model predicted shape changes with an L1 error comparable to the MRI slice thickness (0.7mm). Next, an ablation study demonstrated that the introduction of a contrastive term in the loss improved sensitivity of the OA classifier, increasing sensitivity from 0.537 to 0.709, just shy of the upper bound of 0.740 computed on the ground truth bone shape maps. Our approach provides a promising tool, suitable for patient specific OA trajectory analysis.
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