Machine Learning-Based Prediction of Albuminuria in Diabetic Patients Using Retinal Microvascular Metrics on Optical Coherence Tomography Angiography.
- 1Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
- 2RetiMark R&D Center, Seoul, South Korea.
- 3Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Abstract
Purpose
To investigate the association between optical coherence tomography angiography (OCTA) parameters and diabetic nephropathy (DN) in adults with diabetes and to evaluate the incremental predictive value of OCTA-derived retinal microvascular metrics using machine learning-based models.Methods
This prospective observational study included adults with diabetes who underwent systemic evaluation and OCTA imaging. Vessel density (VD), perfusion density (PD), and foveal avascular zone (FAZ) metrics were quantified from OCTA images. DN severity was classified based on the urine albumin-to-creatinine ratio (UACR). Associations between OCTA parameters and albuminuria were assessed using regression analyses. For prediction of albuminuria, machine learning models were trained using clinical variables alone and with the addition of OCTA parameters, and model performance was evaluated.Results
A total of 388 eyes were analyzed. VD and PD decreased progressively with increasing albuminuria severity (all P < 0.015), whereas central OCTA measurements and FAZ metrics did not significantly differ across groups (all P > 0.10). In regression analyses, higher UACR was associated with reduced VD and PD in the central, outer, and whole regions (all P < 0.05), while marginal associations were observed in the inner region. In machine learning analyses, inclusion of OCTA parameters improved albuminuria prediction in four of the six algorithms (Δ area under the curve [AUC], 0.002-0.027), with the highest performance observed for the decision tree classifier (AUC, 0.754; 95% confidence interval, 0.620-0.865).Conclusions
OCTA parameters are significantly associated with albuminuria in diabetes, and their inclusion in predictive models improves albuminuria identification, supporting OCTA as a noninvasive complementary tool for early detection of DN.Translational relevance
OCTA can be integrated into clinical practice as a noninvasive method for early detection of DN, aiding in timely intervention.
Related
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- Evaluating the real-world accessibility of open-source deep learning models for retinal vessel segmentation in optical coherence tomography angiography
BibTeX
@article{song2026machine,
title = {{Machine Learning-Based Prediction of Albuminuria in Diabetic Patients Using Retinal Microvascular Metrics on Optical Coherence Tomography Angiography.}},
author = {Jae Shin Song and Shubham Borghare and Seoyoon Choi and Sang Jun Park and Soo Lim and Sejoong Kim and Se Joon Woo},
journal = {Translational vision science \& technology},
year = {2026},
month = oct,
doi = {10.1167/tvst.15.10.9},
url = {https://europepmc.org/article/MED/42847594},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Machine Learning-Based Prediction of Albuminuria in Diabetic Patients Using Retinal Microvascular Metrics on Optical Coherence Tomography Angiography.
AU - Jae Shin Song
AU - Shubham Borghare
AU - Seoyoon Choi
AU - Sang Jun Park
AU - Soo Lim
AU - Sejoong Kim
AU - Se Joon Woo
PY - 2026
DA - 2026/10/01
JO - Translational vision science & technology
DO - 10.1167/tvst.15.10.9
UR - https://europepmc.org/article/MED/42847594
AB - Purpose To investigate the association between optical coherence tomography angiography (OCTA) parameters and diabetic nephropathy (DN) in adults with diabetes and to evaluate the incremental predictive value of OCTA-derived retinal microvascular metrics using machine learning-based models.Methods This prospective observational study included adults with diabetes who underwent systemic evaluation and OCTA imaging. Vessel density (VD), perfusion density (PD), and foveal avascular zone (FAZ) metrics were quantified from OCTA images. DN severity was classified based on the urine albumin-to-creatinine ratio (UACR). Associations between OCTA parameters and albuminuria were assessed using regression analyses. For prediction of albuminuria, machine learning models were trained using clinical variables alone and with the addition of OCTA parameters, and model performance was evaluated.Results A total of 388 eyes were analyzed. VD and PD decreased progressively with increasing albuminuria severity (all P < 0.015), whereas central OCTA measurements and FAZ metrics did not significantly differ across groups (all P > 0.10). In regression analyses, higher UACR was associated with reduced VD and PD in the central, outer, and whole regions (all P < 0.05), while marginal associations were observed in the inner region. In machine learning analyses, inclusion of OCTA parameters improved albuminuria prediction in four of the six algorithms (Δ area under the curve [AUC], 0.002-0.027), with the highest performance observed for the decision tree classifier (AUC, 0.754; 95% confidence interval, 0.620-0.865).Conclusions OCTA parameters are significantly associated with albuminuria in diabetes, and their inclusion in predictive models improves albuminuria identification, supporting OCTA as a noninvasive complementary tool for early detection of DN.Translational relevance OCTA can be integrated into clinical practice as a noninvasive method for early detection of DN, aiding in timely intervention.
SN - 2164-2591
ER -