Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks.

ABSTRACT:

Purpose

To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists.

Materials and methods

Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades.

Results

With committee scores used as ground truth, the model

SUBMITTER: Thomas KA 

PROVIDER: S-EPMC7104788 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature