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.
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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.
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