Saleh, Radhwan A.A.Al-Sameai, HabebCordón Ciordia, BeatrizGarcía-Martín, ElenaOrtega Hortas, MarcosMoura, Joaquim de2026-10-092026-10-092026-11R.A.A. Saleh, H. Al-Sameai, B. Cordon, E. Garcia-Martin, M. Ortega, and J. de Moura, "Segmentation-Guided Multi-Task Learning for Automated Analysis of Post-Covid-19 Retinal Alterations in Oct Images", Engineering Science and Technology, an International Journal, Vol. 83, 102536, Nov. 2026, https://doi.org/10.1016/j.jestch.2026.1025362215-0986https://hdl.handle.net/2183/49660The source code for the experiments conducted in this research is available upon reasonable request to the corresponding author.[Abstract] 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.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Post-COVID-19 syndromeRetinal microvasculatureStructural changesShap valuesOptical coherence tomography (OCT)Multi-task deep learningXAIjournal articleopen access10.1016/j.jestch.2026.102536