Deep Learning-based age prediction models from retinal Optical Coherence Tomography images

Abstract

This study evaluates the potential of Optical Coherence Tomog raphy (OCT) as a non-invasive tool for retinal age prediction in healthy individuals. A dataset comprising 1,180 eyes from 517 con trol subjects was used to compare deep learning models trained on different OCT scan types: peripapillary B-scans, individual macula raster B-Scans, and full macular volumes. Images underwent stan dardized preprocessing, and models based on 2D and 3D ResNet architectures were trained and optimized using Transfer Learning. Results show that volumetric macular scans applied in a ResNet 3D model achieved the lowest Mean Absolute Error (3.07 years), outperforming both previous literature and all tested 2D configura tions. Overall, findings highlight that integrating depth and spatial features in OCT data significantly enhances retinal age estimation.