Deep Learning–based Estimation of Glomerular Filtration Rate from Macular Optical Coherence Tomography

Purpose: This study aimed to develop a deep learning regression model using macular optical coherence tomography (OCT) images to predict estimated glomerular filtration rate (eGFR).Methods: In this retrospective cross-sectional study, we analyzed data from patients who underwent preoperative evaluation for cataract surgery, including spectral-domain OCT (Spectralis; Heidelberg Engineering) images, blood test-derived eGFR, and ocular biometry.A dual-input regression model based on a modified ResNet-18 architecture was constructed to process simultaneously horizontal and vertical B-scan macular OCT images.The model's predicted eGFR was further combined with patient age, sex, presence of diabetes and hypertension, and axial length using a Random Forest regression algorithm to create a hybrid model.Model performance was assessed using the root mean squared error (RMSE) and coefficient of determination (R 2 ).Gradient-weighted regression activation mapping (Grad-RAM) was applied for visualization to assess the anatomical regions contributing to predictions.Results: A total of 101 eyes from 101 patients were included.The ResNet-18 model achieved an eGFR prediction accuracy with an R 2 of 0.89 and an RMSE of 4.23.The Random Forest hybrid model further improved predictive performance, achieving an R 2 of 0.94 and an RMSE of 3.64.The mean absolute error (MAE) across the full cohort was 3.69 2.08 mL/min/1.73m 2 .Among the 10 most accurately predicted cases, the MAE was as low as 0.42 0.20 mL/min/1.73m 2 , while the 10 least accurate cases showed an MAE of 7.44 0.92 mL/ min/1.73m 2 .Grad-RAM visualizations revealed predominant activation in the choroidal region of vertical OCT images.Conclusions: This is the first study to demonstrate accurate prediction of renal function from macular OCT images using deep learning.The dual-input architecture and Grad-RAM visualization enabled high prediction performance and partial anatomical interpretability.These findings support the potential of OCT-based oculomics for systemic disease assessment.