ReviewFrontiers in immunology2026
Application of artificial intelligence in differentiating IgG4-related ophthalmic disease and orbital MALT lymphoma: a review of radiomics and deep learning advances.
Review in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The differentiation between Immunoglobulin G4-related ophthalmic disease (IgG4-ROD) and orbital lymphoma, particularly the mucosa-associated lymphoid tissue (MALT) subtype, presents a significant clinical challenge due to overlapping imaging features and similar presentations. Recent advances in artificial intelligence (AI), particularly radiomics and deep learning, have shown promising potential in enhancing diagnostic accuracy by extracting high-dimensional imaging features and constructing robust predictive models. This review systematically examines the current state of AI applications in distinguishing IgG4-ROD from orbital MALT lymphoma, highlighting key methodologies in image-based feature extraction, model development, and diagnostic performance evaluation. We explore various AI techniques applied to multimodal imaging data integration and discuss optimization strategies for deep learning architectures tailored to this clinical context. Additionally, the review addresses the practical challenges and limitations of translating AI-assisted diagnostic tools into routine clinical practice, including issues related to small sample sizes, retrospective single-center designs, data variability, interpretability, and the critical need for robust external validation. By synthesizing recent research findings, this review aims to provide a comprehensive overview of AI-driven diagnostic advances, critically assess current challenges, and propose future directions to improve the accuracy and reliability of orbital disease differentiation, ultimately supporting more precise clinical decision-making.
Indexed as
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.