Objective:
To explore the use of artificial intelligence (AI) in predicting conversion to neovascular age-related macular degeneration (nAMD) from geographic atrophy (GA) using various biomarkers, including hyperreflective foci volume and inner retinal thickness.
Key Findings:
- Four significant predictors of conversion to nAMD identified: hyperreflective foci volume, inner retinal thickness, retinal pigment epithelium to drusen complex thickness, and presence of subretinal drusenoid deposits, highlighting the importance of these biomarkers in clinical assessments.
- GA size, pigment epithelial detachment volume, and certain drusen types were not significant predictors, indicating a need for focused biomarker analysis.
- The highest hazard ratios were associated with hyperreflective foci volume and subretinal drusenoid deposits, increasing risk by 1.2 to 1.3 times, underscoring their critical role in risk assessment.
Interpretation:
AI can effectively analyze large datasets to identify key biomarkers that predict the risk of conversion to nAMD, potentially improving patient monitoring and clinical trial design.
Limitations:
- The study relied on retrospective data, which may not fully capture the complexities of real-world scenarios, potentially limiting the generalizability of the findings.
- The analysis did not assess the additive effects of multiple biomarkers, which could provide a more comprehensive understanding of conversion risk.
Conclusion:
AI has the potential to enhance patient management and enrich clinical trials by identifying high-risk populations for conversion to nAMD, which may lead to better therapeutic outcomes and more tailored patient care.
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