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Updated: May 12, 2025
In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
Published on: July 24, 2020
Retinal and choroidal microvasculature and structural analysis in OCTA for refractive amblyopia diagnosis using
Xinlong Liu1, Caihong Xue1, Mengdi Li2
1Clinical College of Ophthalmology, Tianjin Medical University, Tianjin 300020, China; Tianjin Key Lab of Ophthalmology and Vision Science, Tianjin Eye Institute, Tianjin Eye Hospital, Tianjin 300020, China.
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.
