Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis.

ABSTRACT:

Objective

The purpose of this research was to develop a deep-learning model to assess radiographic finger joint destruction in RA.

Methods

The model comprises two steps: a joint-detection step and a joint-evaluation step. Among 216 radiographs of 108 patients with RA, 186 radiographs were assigned to the training/validation dataset and 30 to the test dataset. In the training/validation dataset, images of PIP joints, the IP joint of the thumb or MCP joints were manually clipped and scored for joint space narrowing (JSN) and bone erosion by clinicians, and then these images were augmented. As a result, 11 160 images were used to train and validate a deep convolutional neural network for joint evaluation. Three thousand seven hundred and twenty selected images were used to trai

SUBMITTER: Hirano T 

PROVIDER: S-EPMC6921374 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature