Carneiro, Ângela MariaFaria, Brígida MónicaSousa, Vânia Guimarães de2025-02-132025-02-132024-11-292024-11-29http://hdl.handle.net/10400.22/29502Age-related Macular Degeneration (AMD) is a significant cause of vision loss, particularly in its exudative form, where abnormal blood vessel growth and fluid buildup in the retina occur. Anti-Vascular Endothelial Growth Factor (anti-VEGF) intravitreal injections have improved outcomes for exudative AMD, though patient responses vary, and the treatment burden is considerable due to frequent injections. This study aimed to identify Optical Coherence Tomography (OCT) biomarkers and clinical factors that predict treatment response in exudative AMD, analyzing data over three years. By applying statistical and machine learning methods, particularly supervised learning models like decision trees, biomarkers that significantly influenced outcomes were identified, such as choroidal thickness, neovascular membrane type, and fluid localization, among others. The decision tree model demonstrated good predictive accuracy (71.7%) and precision (75.8%). The findings suggest that OCT biomarkers can be instrumental in guiding personalized treatment strategies and optimizing anti-VEGF therapy to enhance patient outcomes while reducing the frequency of injections. This approach helps identify patients less likely to respond to standard treatments, facilitating more individualized care that improves clinical outcomes and quality of life for those with exudative AMD.engAMDAnti-VEGF treatmentOCT biomarkersMachine learningmaster thesis203852494