A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.

ABSTRACT:

Background

The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.

Methods

Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months.

Results

Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to

SUBMITTER: Schiratti JB 

PROVIDER: S-EPMC8521982 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature