Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis.

ABSTRACT:

Objectives

To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.

Methods

Hand MRIs (coronal T1-weighted, T2-weighted fat-suppressed, T1-weighted fat-suppressed contrast-enhanced) of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients from the rheumatology department of the Erlangen University Hospital were assessed by two expert rheumatologists using the Outcome Measures in Rheumatology-validated RA MRI Scoring System and PsA MRI Scoring System scores and were used to train, validate and test CNNs to automatically score erosions, osteitis and synovitis. Scoring performance was compared with human

SUBMITTER: Schlereth M 

PROVIDER: S-EPMC11184189 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature