Analysis of choroidal OCT intensity and profile changes in high myopia: correlation with visual impairment and identification of pathological myopia

Aims To investigate the changes in choroidal optical coherence tomography (OCT) radiomic features, their correlations with visual acuity and utility in identifying pathological myopia (PM). Methods A total of 288 myopic participants aged 18–50 years were included. Choroidal radiomic features were extracted and screened from OCT images via PyRadiomics and machine learning. Selected features were analysed for their associations with axial length, spherical equivalent, age and best corrected visual acuity (BCVA). Classification models were built using these features and their performance to identify PM was evaluated and compared with clinical parameters through five-fold cross-validation using metrics including area under the curve (AUC), accuracy, recall and F1 score. Results A total of 464 radiomic features were extracted and four choroidal intensity and shape features significantly correlated with axial length (p<0.001) were selected. Smaller maximum diameter, higher coarseness and greater perimeter surface ratio were significantly associated with worse BCVA (p<0.05). Radiomic features showed better performance than clinical features in both the internal test set (AUC=0.970 vs 0.938) and external validation set (AUC=0.858 vs 0.753) in identifying PM. Combining radiomic features with risk factors improved classification performance (AUC=0.990 internally and 0.892 externally). Among the included factors, intensity feature was most predictive for PM. Conclusions OCT-based choroidal intensity and shape features were significantly correlated with axial elongation and visual impairment and outperformed clinical parameters in identifying PM. These features could serve as reliable biomarkers for monitoring high myopia progression.