Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD

To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years.

The dataset of the Age-related Eye Disease Study (AREDS) was used to train and validate our prediction model. External validation was performed on the Nutritional AMD Treatment-2 (NAT-2) study.

An ensemble of deep learning screening methods was trained and validated on 116,875 color fundus photos from 4139 participants in the AREDS study to classify them as no, early, intermediate, or advanced AMD and further stratified them along the AREDS 12 level severity scale. Second step: the resulting AMD scores were combined with sociodemographic clinical data and other automatically extracted imaging data by a logistic model tree machine learning technique to predict risk for progression to late AMD within 1 or 2 years, with training and validation performed on 923 AREDS participants who progressed within 2 years, 901 who progressed within 1 year, and 2840 who did not progress within 2 years. For those found at risk of progression to late AMD, we further predicted the type (dry or wet) of the progression of late AMD.

For identification of early/none vs. intermediate/late (i.e., referral level) AMD, we achieved 99.2% accuracy. The prediction model for a 2-year incident late AMD (any) achieved 86.36% accuracy, with 66.88% for late dry and 67.15% for late wet AMD. For the NAT-2 dataset, the 2-year late AMD prediction accuracy was 84%.

Validated color fundus photo-based models for AMD screening and risk prediction for late AMD are now ready for clinical testing and potential telemedical deployment.

Noninvasive, highly accurate, and fast AI methods to screen for referral level AMD and to predict late AMD progression offer significant potential improvements in our care of this prevalent blinding disease.