DEEP LEARNING FOR AUTOMATIC PREDICTION OF EARLY ACTIVATION OF TREATMENT-NAIVE NONEXUDATIVE MACULAR NEOVASCULARIZATIONS IN AGE-RELATED MACULAR DEGENERATION.

BACKGROUND

Around 30% of nonexudative macular neovascularizations exudate within 2 years from diagnosis in patients with age-related macular degeneration. The aim of this study is to develop a deep learning classifier based on optical coherence tomography (OCT) and OCT angiography (OCTA) to identify nonexudative macular neovascularizations at risk of exudation.

METHODS

Patients with age-related macular degeneration showing OCTA and fluorescein angiography-documented nonexudative macular neovascularization with a 2-year minimum imaging follow-up were retrospectively selected. Patients showing OCT B-scan-documented macular neovascularization exudation within the first 2 years formed the EX GROUP while the others formed the QU GROUP. ResNet-101, Inception-ResNet-v2, and DenseNet-201 were independently trained on OCTA and OCT B-scan images. Combinations of the six models were evaluated with major and soft voting techniques.

RESULTS

Eighty-nine eyes of 89 patients with a follow-up of 5.7 ± 1.5 years were recruited (35 EX GROUP and 54 QU GROUP). Inception-ResNet-v2 was the best performing among the three single convolutional neural networks. The major voting model resulting from the association of the three different convolutional neural networks resulted in an improvement of performance both for OCTA and OCT B-scan (both significantly higher than human graders' performance). The soft voting model resulting from the combination of OCTA and OCT B-scan-based major voting models showed a testing accuracy of 94.4%. Peripheral arcades and large vessels on OCTA en face imaging were more prevalent in the QU GROUP.

CONCLUSION

Artificial intelligence shows high performances in identifications of nonexudative macular neovascularizations at risk for exudation within the first 2 years of follow-up, allowing better customization of follow-up timing and avoiding treatment delay. Better results are obtained with the combination of OCTA and OCT B-scan image analysis.