Segmentation-guided multi-task learning for automated analysis of post-COVID-19 retinal alterations in OCT images

The COVID-19 pandemic has revealed significant systemic effects beyond the respiratory system, including structural alterations in the retina. This study proposes explainable, segmentation-guided multi-task deep learning for detecting post-COVID retinal abnormalities using OCT. A newly compiled dataset of 225 participants (healthy controls, non-persistent COVID-19, and persistent post-COVID sequelae) is analyzed using a two-stage framework that jointly optimizes disease classification and pixel-wise retinal layer segmentation. Seven deep learning backbones are evaluated across five experimental setups. The proposed two-stage multi-task learning framework enhances classification accuracy by +1.08% and F1-score by +1.91% compared to the classification-only baseline, with the segmentation branch improving feature robustness by guiding the encoder toward anatomically meaningful retinal structures. These findings demonstrate the potential of segmentation-guided multi-task deep learning for automated post-COVID retinal image analysis while highlighting that retinal imaging alone may not sufficiently discriminate between persistent and non-persistent symptom profiles.