Choroidal Neovascularization

Choroidal neovascularization (CNV) is characterized by abnormal blood-vessel growth beneath or within the retina and is a major cause of vision loss, particularly in neovascular age-related macular degeneration. Other associated conditions include pathologic myopia, ocular histoplasmosis, and inflammatory eye disease. Diagnosis and management depend on retinal imaging, clinical examination, and assessment of fluid, hemorrhage, fibrosis, and visual function.

Clinical practice is moving from reactive treatment of visible disease activity toward proactive monitoring and individualized treatment intervals. Optical coherence tomography has become central to detecting retinal fluid and guiding decisions, while combination imaging can improve characterization of lesions and long-term structural damage. Durable disease control, treatment burden, preservation of functional vision, and management of fibrosis remain important priorities.

Artificial intelligence is increasingly being evaluated for automated image interpretation, lesion classification, fluid quantification, and prediction of treatment response. Its most practical near-term role is decision support that helps clinicians prioritize scans, identify subtle activity, and standardize longitudinal assessments. Safe adoption requires representative training data, external validation, transparent performance reporting, clinician oversight, and safeguards against automation bias.

North America and Europe generally benefit from established retinal-specialist networks, advanced imaging, and broader access to intravitreal therapy, although treatment adherence and healthcare affordability remain challenges. Asia-Pacific combines sophisticated ophthalmic centers with substantial rural and regional access gaps. Latin America faces variation in specialist availability, reimbursement, and continuity of care. The Middle East is expanding advanced ophthalmic services unevenly across countries, while Africa continues to face shortages of retinal specialists, diagnostic equipment, and sustained treatment pathways. Across all regions, earlier referral, imaging capacity, and reliable follow-up are central determinants of outcomes.

ASEAN countries show wide variation in ophthalmic infrastructure, reimbursement, and urban–rural access. BRICS members span highly diverse public and private care models, creating opportunities for local manufacturing, workforce development, and standardized clinical pathways. The European Union benefits from cross-border scientific collaboration but retains differences in reimbursement and service delivery. G7 systems have strong research and specialist capacity while confronting aging populations and treatment workload. GCC countries are investing in advanced healthcare infrastructure, whereas NATO members reflect diverse national systems and should prioritize interoperable referral, imaging, and outcomes frameworks.

Australia and Canada combine advanced retinal services with geographic access challenges. Brazil and Mexico face major differences between metropolitan and underserved areas. China and India have rapidly expanding ophthalmic capacity but substantial regional variation in specialist access and affordability. France, Germany, Italy, Spain, and the United Kingdom possess established retinal care networks, with differences in waiting times, reimbursement, and service organization. Japan and South Korea have strong imaging and specialist capabilities alongside aging-population pressures. Russia’s access and care continuity vary across regions. The United States has extensive specialist capacity, but cost, insurance design, and adherence remain significant considerations.

Industry leaders should support referral pathways that connect primary eye care with retinal specialists, expand access to high-quality optical coherence tomography, and design care models around adherence and treatment burden. Evidence generation should emphasize functional vision, anatomy, durability, fibrosis, and patient-reported outcomes rather than isolated short-term imaging changes. Partnerships with health systems can improve clinic workflow, interval monitoring, and outreach to underserved communities. Artificial intelligence should be introduced through validated, auditable decision-support tools with clear accountability and clinician training.

This summary uses a structured review of established clinical knowledge concerning CNV, including disease mechanisms, diagnostic imaging, treatment principles, health-system access, and emerging use of artificial intelligence. Regional, group, and country observations are framed around documented differences in healthcare infrastructure, specialist availability, demographic pressure, reimbursement, and continuity of care. Claims are kept qualitative, and no market estimates, forecasts, market shares, or company-specific assessments are used.

CNV management is being shaped by earlier detection, increasingly precise imaging, individualized treatment strategies, and the need to reduce long-term care burden. Artificial intelligence can strengthen consistency and monitoring when deployed with rigorous validation and clinical oversight. Sustainable progress will depend on combining innovation with workforce development, equitable access, reliable follow-up, and outcome-focused care across regions and health systems.