Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.
K, D., Y, Z., & Z, W. (2026). Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.. Frontiers in neurology. https://doi.org/10.3389/fneur.2026.1885303
K D, Y Z, Z W. Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.. Frontiers in neurology. 2026; doi: 10.3389/fneur.2026.1885303
K D, Y Z, Z W. Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.[J]. Frontiers in neurology. 2026. DOI: 10.3389/fneur.2026.1885303.
@article{k2026,
author = {Du K and Zheng Y and Wang Z},
title = {Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.},
journal = {Frontiers in neurology},
year = {2026},
doi = {10.3389/fneur.2026.1885303},
note = {PMID: 42656234},
}
TY - JOUR AU - Du K AU - Zheng Y AU - Wang Z TI - Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases. T2 - Frontiers in neurology PY - 2026 DO - 10.3389/fneur.2026.1885303 AN - PMID:42656234 ER -
BACKGROUND: We developed and validated RetinalVNG-Net, a multimodal deep learning framework for simultaneous risk stratification of hypertensive retinopathy, diabetic retinopathy, and neurodegenerative-associated retinal changes from integrated fundus photography, optical coherence tomography, and clinical metadata. METHODS: This retrospective multi-center study included 2,740 subjects from three independent ophthalmology centers (January 2019-December 2023). Centers A and B (n = 2,220) constituted the development set, with a stratified 15% subset (n = 333) reserved for hyperparameter tuning and the remainder (n = 1,887) used for five-fold cross-validation. Center C (n = 520) served as a geographically distinct, device-heterogeneous external test set. RetinalVNG-Net employs a RETFound ViT-Large fundus encoder, a dual-stream Optical Coherence Tomography (OCT) branch (ResNet-3D-18 for volumetric B-scans and 2D-CNN for layer thickness maps), and a tabular transformer for metadata, fused via cross-modal attention. An auxiliary regression head outputs a continuous Retinal Biological Age Gap (RBAG) score as an interpretable severity biomarker. RESULTS: Internal cross-validation yielded macro-averaged AUC-ROC 0.957 (±0.008), sensitivity 0.913, specificity 0.941, and F1 0.908 across four classes. On the external test set, macro-averaged AUC-ROC reached 0.944 (95% CI 0.922-0.958), with per-class AUCs of 0.963 (hypertensive retinopathy), 0.951 (diabetic retinopathy), 0.924 (neurodegenerative changes), and 0.938 (controls). RetinalVNG-Net significantly outperformed the best single-modality fundus model (macro-AUC 0.944 vs. 0.901; p < 0.001). CONCLUSION: RetinalVNG-Net demonstrates promising robustness and generalizability for simultaneous multimodal risk stratification of vascular and neurodegenerative retinal changes across diverse devices and settings, supporting further evaluation as a tool for risk stratification of retinal manifestations associated with systemic vascular and neurodegenerative disease. Prospective longitudinal studies would be required to establish value for early or predictive detection.