z-logo
open-access-imgOpen Access
Multilevel Modeling of Joint Damage in Rheumatoid Arthritis
Author(s) -
Li Hongyang,
Guan Yuanfang
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200184
Subject(s) - joint (building) , rheumatoid arthritis , computer science , artificial intelligence , damages , machine learning , deep learning , support vector machine , limit (mathematics) , software , medicine , engineering , mathematics , architectural engineering , mathematical analysis , law , political science , programming language
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here