loading page

Multilevel Modeling of Joint Damage in Rheumatoid Arthritis
  • Hongyang Li,
  • Yuanfang Guan
Hongyang Li

Corresponding Author:[email protected]

Author Profile
Yuanfang Guan
Author Profile


While most deep learning approaches are developed for single images, in real world applications, images are often obtained as a series to inform decision making. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. In this study, we present an approach that seamlessly integrates deep learning and traditional machine learning models, to study multiple images and score joint damages in rheumatoid arthritis. This method allows the quantification of joining space narrowing to approach the clinical upper limit. Beyond predictive performance, we integrate the multilevel interconnections across joints and damage types into the machine learning model and reveal the cross-regulation map of joint damages in rheumatoid arthritis.
Corresponding author(s) Email:   [email protected] or [email protected]
16 Jul 2022Submitted to AISY Interactive Papers
18 Jul 2022Published in AISY Interactive Papers
13 Oct 2022Published in Advanced Intelligent Systems on pages 2200184. 10.1002/aisy.202200184