| Summary: | 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. 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.
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