GENERATIVE DEEP LEARNING APPROACH TO PREDICT POSTTREATMENT OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AGE-RELATED MACULAR DEGENERATION AFTER 12 MONTHS.

PURPOSE

Predicting long-term anatomical responses in neovascular age-related macular degeneration patients is critical for patient-specific management. This study validates a generative deep learning model to predict 12-month posttreatment optical coherence tomography (OCT) images and evaluates the impact of incorporating clinical data on predictive performance.

METHODS

A total of 533 eyes from 513 treatment-naïve neovascular age-related macular degeneration patients were analyzed. A conditional generative adversarial network served as the baseline model, generating 12-month OCT images using pretreatment OCT, fluorescein angiography, and indocyanine green angiography. We then sequentially added OCT after three loading doses, baseline visual acuity, treatment regimen (pro re nata or treat-and-extend), drug type, and switching events. The generated and patient OCT images were compared for intraretinal fluid, subretinal fluid, pigment epithelial detachment, and subretinal hyperreflective material, both qualitatively and quantitatively.

RESULTS

The baseline model achieved acceptable accuracy for 4 macular fluid compartments (range 0.74-0.96). Incorporating OCT after loading doses and other clinical parameters improved accuracy (range 0.91-0.98). With all the clinical inputs, the model achieved 92% accuracy in distinguishing wet macular status from dry macular status. The segmented fluid compartments in the generated images correlated positively with those in the patient images.

CONCLUSION

Integrating clinical and treatment data, particularly OCT data after loading doses, significantly enhanced the 12-month predictive performance of conditional generative adversarial networks. This approach can help clinicians anticipate anatomical outcomes and guide personalized, long-term neovascular age-related macular degeneration treatment strategies.