ArticleAdvanced intelligent systems (Weinheim an der Bergstrasse, Germany)2022
Multilevel Modeling of Joint Damage in Rheumatoid Arthritis.
Article in Advanced intelligent systems (Weinheim an der Bergstrasse, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review.Therapeutic advances in musculoskeletal disease · 2026Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.