Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.

ABSTRACT:

Objective

To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.

Methods

Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint tra

SUBMITTER: Guan B 

PROVIDER: S-EPMC7137777 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature