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

OBJECTIVE

To evaluate the features of retinal and choroidal microcirculation and structure in patients with amblyopia compared to healthy adolescents of the same age (>10 years old). To classify and diagnose amblyopia using machine learning techniques on optical coherence tomographic angiography (OCTA) images.

METHODS

Nineteen adolescents aged 11-17 with hyperopic refractive amblyopia and 22 age-matched healthy controls underwent 12 × 12 mm macular OCTA scans. The eyes were classified into three groups: amblyopic, contralateral non-amblyopic, and control. Retinal thickness (RT), choroidal thickness (ChT), and perfusion densities in the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were measured across nine regions. A combination of statistical analysis and machine learning, including cross-validation and Random Forest classification, was used to enhance the diagnostic accuracy and classify amblyopic and normal eyes.

RESULTS

Retinal thickness was significantly higher in the amblyopic eyes compared to the control group in multiple regions, including the central (p < 0.001), nasal (p < 0.01), and temporal zones(p < 0.01). Choroidal thickness was also greater in the amblyopic eyes, particularly in the central and nasal regions (p < 0.05). However, no significant differences were observed in the perfusion densities of SCP and DCP. The machine learning classification model incorporating cross-validation achieved an accuracy of 92%, with Random Forest demonstrating improved classification and feature importance analysis.

CONCLUSION

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.