Visual Field-Guided Entangled Identifies Clinically Dis-tinct Glaucoma Endophenotypes and Novel Risk Loci
Moradi, M.; Chen, L.; Zhao, Y.; Bineshfar, N.; Sekimitsu, S.; Eslami, M.; Elze, T.; Zebardast, N.
Show abstract
Glaucoma phenotyping remains challenging due to disease heterogeneity and single-modality limitations. We introduce a visual field (VF)-guided entangled learning framework that integrates structural and functional signals during training to learn functionally informed macular retinal nerve fiber layer (mRNFL) representations while enabling OCT-only inference. In 5,372 paired MEEI examinations, VF-guided phenotyping identified 9 clinically distinct mRNFL phenotypes with divergent progression rates (MD slopes -0.2 to -1.8 dB/year, P <0.001), improving clustering over OCT-only by 22% (FCM) and 11% (GMM). External evaluation in 74,077 UK Biobank images confirmed generalizability, with improved risk association (r=-0.33 vs r=0.04). Genetic analyses identified 12 additional glaucoma loci compared with OCT-only phenotyping. VF-guided entangled learning improves clinically and genetically coherent mRNFL phenotyping with broad applicability to multimodal medical imaging.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interpretable Detection of Epiretinal Membrane from Optical Coherence Tomography with Deep Neural Networks 95%
- Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography 93%
- Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye 93%
Similar papers in this journal
- Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans 96%
- An Inherently Interpretable AI model improves Screening Speed and Accuracy for Early Diabetic Retinopathy 94%
- Assessing generalizability of an AI-based visual test for cervical cancer screening 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.