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

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Conditions studied: Myopic Choroidal Neovascularization

In brief

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.

Key facts

Study ID: NCT07402629
Run by: Second Affiliated Hospital, Zhejiang University, School of Medicine
People needed: 110
Starts: 2026-03-01
Expected to finish: 2028-12-31
Last updated by the study team: 2026-02-11

Who can join

Age: 18 and older, up to 70. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

Myopic spherical equivalent greater than -6.0 diopters and/or axial length > 26.5 mm.
Patients presenting with myopic fundus changes, as defined by the International Photographic Classification System for Myopic Maculopathy.
Completion of a comprehensive ophthalmic examination at initial diagnosis, including Best Corrected Visual Acuity , slit-lamp examination, dilated fundus examination, intraocular pressure measurement, refractometry, axial length measurement, color fundus photography, Optical Coherence Tomography, OCT Angiography, and Fundus Fluorescein Angiography.
Patients with active mCNV at initial diagnosis who are treatment-naïve.
Final inclusion of all images required confirmation by two retinal specialists. Images with discrepant assessments were adjudicated by a senior researcher to reach a final decision.

You may not qualify if…

Patients with choroidal neovascularization (CNV) due to causes other than myopia, such as age-related macular degeneration, adult-onset foveomacular vitelliform dystrophy, multifocal choroiditis, punctate inner choroidopathy, or retinoschisis.
Poor-quality OCTA images, for example, those containing projection artifacts from overlying vessels at the mCNV lesion site, or excessively dark images dominated by thick outer choroidal vessels.
Patients with a history of intraocular surgery, such as prior pars plana vitrectomy.
Subjects who developed any of the above conditions during the follow-up period or who requested to withdraw from the study were removed from the experimental cohort.

Full record on ClinicalTrials.gov

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