Synthesizing Bidirectional Temporal States of Knee Osteoarthritis
  Radiographs with Cycle-Consistent Generative Adversarial Neural Networks
Fabi Prezja Leevi Annala Sampsa Kiiskinen Suvi Lahtinen Timo Ojala
Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is
challenging to detect early due to subtle radiographic indicators. Diverse,
extensive datasets are needed but are challenging to compile because of
privacy, data collection limitations, and the progressive nature of KOA.
However, a model capable of projecting genuine radiographs into different OA
stages could augment data pools, enhance algorithm training, and offer
pre-emptive prognostic insights. In this study, we trained a CycleGAN model to
synthesize past and future stages of KOA on any genuine radiograph. The model
was validated using a Convolutional Neural Network that was deceived into
misclassifying disease stages in transformed images, demonstrating the
CycleGAN's ability to effectively transform disease characteristics forward or
backward in time. The model was particularly effective in synthesizing future
disease states and showed an exceptional ability to retroactively transition
late-stage radiographs to earlier stages by eliminating osteophytes and
expanding knee joint space, signature characteristics of None or Doubtful KOA.
The model's results signify a promising potential for enhancing diagnostic
models, data augmentation, and educational and prognostic usage in healthcare.
Nevertheless, further refinement, validation, and a broader evaluation process
encompassing both CNN-based assessments and expert medical feedback are
emphasized for future research and development.