Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints.

ABSTRACT:

Objective

To study the classification performance of a pre-trained convolutional neural network (CNN) with transfer learning by artificial joint ultrasonography images in rheumatoid arthritis (RA).

Methods

This retrospective study focused on abnormal synovial vascularity and created 870 artificial joint ultrasound images based on the European League Against Rheumatism/Outcome Measure in Rheumatology scoring system. One CNN, the Visual Geometry Group (VGG)-16, was trained with transfer learning using the 870 artificial images for initial training and the original plus five additional images for second training. The models were then tested for the ability to classify joints using real joint ultrasound images obtained from patients with RA. The study was registered in UMIN Clin

SUBMITTER: Fukae J 

PROVIDER: S-EPMC11795604 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature