English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, ischallenging to detect early due to subtle radiographic indicators. Diverse,extensive datasets are needed but are challenging to compile because ofprivacy, data collection limitations, and the progressive nature of KOA.However, a model capable of projecting genuine radiographs into different OAstages could augment data pools, enhance algorithm training, and offerpre-emptive prognostic insights. In this study, we trained a CycleGAN model tosynthesize past and future stages of KOA on any genuine radiograph. The modelwas validated using a Convolutional Neural Network that was deceived intomisclassifying disease stages in transformed images, demonstrating theCycleGAN's ability to effectively transform disease characteristics forward orbackward in time. The model was particularly effective in synthesizing futuredisease states and showed an exceptional ability to retroactively transitionlate-stage radiographs to earlier stages by eliminating osteophytes andexpanding knee joint space, signature characteristics of None or Doubtful KOA.The model's results signify a promising potential for enhancing diagnosticmodels, data augmentation, and educational and prognostic usage in healthcare.Nevertheless, further refinement, validation, and a broader evaluation processencompassing both CNN-based assessments and expert medical feedback areemphasized for future research and development.

Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks