Subchondral Bone Length in Knee Osteoarthritis: A Deep Learning-Derived Imaging Measure and Its Association With Radiographic and Clinical Outcomes.

ABSTRACT:

Objective

To develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity.

Methods

A fast region-based convolutional neural network was trained to crop the knee joints in sagittal dual-echo steady-state magnetic resonance imaging sequences obtained from the Osteoarthritis Initiative (OAI). Publicly available annotations of the cartilage and menisci were used as references to annotate the tibia and the femur in 61 knees. Another deep neural network (U-Net) was developed to learn these annotations. Model predictions were compared to radiologist-driven annotations on an independent test set (27 knees). The U-Net was applied to automatically extract the knee joint structu

SUBMITTER: Chang GH 

PROVIDER: S-EPMC8581065 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature