Multilevel Modeling of Joint Damage in Rheumatoid Arthritis

Abstract

While most deep learning approaches are developed for single images, in real-world applications, images are often obtained as a series to inform decisionmaking. Due to hardware (memory) and software (algorithm) limitations, few methods have been developed to integrate multiple images so far. Herein, an approach that seamlessly integrates deep learning and traditional machine learning models is presented, 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, the multilevel interconnections across joints and damage types into the machine learning model are integrated and the crossregulation map of joint damages in rheumatoid arthritis is revealed. An interactive preprint version of the article can be found at https://doi.org/10.22541/au.165828097.71839600/v1.

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management

“…Nonetheless, hindrances such as imbalances in data record quantities across patients, omissions of pivotal information, and the variability in patient conditions and therapeutic outcomes over time contribute to the complex temporal nature of the data ( 48 ). Conventional ML techniques encounter constraints concerning data pre-processing, time-series analysis capacity, and the simplification of intricate relational processing ( 99 ). Deep learning integrated with structured EHR data, have been deployed to prognosticate disease activity during subsequent outpatient rheumatology consultations, wherein the model trained on the UH cohort manifested an AUC of 0.91 for internal validation and 0.74 for external cohort testing ( 48 ).…”