Retinal and choroidal microvasculature and structural analysis in OCTA for refractive amblyopia diagnosis using machine learning

The results indicate that eyes with refractive amblyopia have notably thicker retinal and choroidal layers, particularly in the central and nasal regions. Combining OCTA data with machine learning creates a strong diagnostic framework for detecting changes in the retina and choroid associated with refractive amblyopia. Utilizing sophisticated classification methods, like Random Forest and cross-validation, improves diagnostic precision and presents new possibilities for automated clinical evaluation.