Deep Learning-Based OCTA Quantification and Modeling for Predicting Long-Term Anti-VEGF Efficacy in mCNV

Plain-language summary

Myopic choroidal neovascularization (mCNV) is one of the sight-threatening complications secondary to pathological myopia. Intravitreal injection of anti-vascular endothelial growth factor (VEGF) is its first-line therapy. However, mCNV is prone to recurrence and long-term visual decline, and there is currently no definitive method for predicting long-term prognosis. This project aims to conduct a long-term follow-up and multi-dimensional quantitative analysis of mCNV using optical coherence tomography angiography (OCTA) images. It seeks to evaluate long-term prognostic indicators, such as recurrence and long-term visual acuity, following anti-VEGF therapy for mCNV, and to construct a deep learning (DL) prediction model for anti-VEGF efficacy based on multi-modal clinical data, ultimately enabling treatment personalization. First, deep learning technology will be utilized to segment and quantitatively analyze mCNV in OCTA images, obtaining multi-dimensional quantitative parameters. These include OCTA-based The mCNV area and vessel junction(VJ). Patients will be followed up regularly for two years post-treatment, monitoring the number of injections, mCNV recurrence, OCT and OCTA quantitative parameters, fundus chorioretinal atrophy lesions, and visual acuity status. A multi-modal DL prediction model will be constructed, primarily based on the multi-dimensional quantitative characteristics of mCNV from OCTA images. This model will aim to identify sensitive indicators for predicting best-corrected visual acuity and recurrence after anti-VEGF therapy for mCNV and to clarify the relationship between therapeutic efficacy, baseline lesion status, and treatment regimen selection. This research will open new avenues for clinically assessing the long-term efficacy of anti-VEGF therapy for mCNV, significantly improve the efficiency and accuracy of efficacy evaluation, and provide a critical reference for personalizing anti-VEGF treatment plans, holding substantial clinical significance.

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