Optical coherence tomography biomarkers for myopic choroidal neovascularization treated with anti-vascular endothelial growth factor

Subretinal hyperreflective material in retinal and chorioretinal disorders: A comprehensive review

Subretinal hyperreflective material (SHRM) is a common and remarkable optical coherence tomography (OCT) biomarker whose importance is emerging in several retinal and chorioretinal diseases, including age-related macular degeneration, central serous chorioretinopathy, polypoidal choroidal vasculopathy, pathologic myopia, posterior uveitis, vitelliform lesions and macular dystrophies, and rarer disorders. Multimodal imaging, also thanks to the introduction of OCT angiography, allowed a deeper characterisation of SHRM components and its morphological changes after treatment, suggesting its usefulness in clinical practice. We discuss and summarize the nature, multimodal imaging characteristics, and prognostic and predictive significance of SHRM in the different retinal and choroidal disorders in which it has been described.

Prediction of Visual Acuity in Pathologic Myopia with Myopic Choroidal Neovascularization Treated with Anti-Vascular Endothelial Growth Factor Using a Deep Neural Network Based on Optical Coherence Tomography Images

Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in patients with mCNV. This study included 279 patients with mCNV at baseline; patient data were collected, including optical coherence tomography (OCT) images, VA, and demographic information. Two models were developed: one comprising horizontal/vertical OCT images (H/V cuts) and the second comprising 25 volume scan images. The coefficient of determination (R2) and root mean square error (RMSE) were computed to evaluate the performance of the trained network. The models achieved high performance in predicting VA after 1 (R2 = 0.911, RMSE = 0.151), 2 (R2 = 0.894, RMSE = 0.254), and 3 (R2 = 0.891, RMSE = 0.227) years. Using multiple-volume scanning, OCT images enhanced the performance of the models relative to using only H/V cuts. This study proposes AI models to predict VA in patients with mCNV. The models achieved high performance by incorporating the baseline VA, OCT images, and post-injection data. This model could assist in predicting the visual prognosis and evaluating treatment outcomes in patients with mCNV undergoing intravitreal anti-vascular endothelial growth factor therapy.