Purpose: To evaluate a machine learning algorithm that allows for computer-aided diagnosis (CAD) of nonadvanced age-related macular degeneration (AMD) by providing an accurate detection and quantification of drusen location, area, and size.
Methods: Color fundus photographs of 407 eyes without AMD or with early to moderate AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically detect and quantify drusen on each image. Based on detected drusen, the CAD software provided a risk assessment to develop advanced AMD. Evaluation of the CAD system was performed using annotations made by two blinded human graders.
Results: Free-response receiver operating characteristics (FROC) analysis showed that the proposed system approaches the performance of human observers in detecting drusen. The estimated drusen area showed excellent agreement with both observers, with mean intraclass correlation coefficients (ICC) larger than 0.85. Maximum druse diameter agreement was lower, with a maximum ICC of 0.69, but comparable to the interobserver agreement (ICC = 0.79). For automatic AMD risk assessment, the system achieved areas under the receiver operating characteristic (ROC) curve of 0.948 and 0.954, reaching similar performance as human observers.
Conclusions: A machine learning system capable of separating high-risk from low-risk patients with nonadvanced AMD by providing accurate detection and quantification of drusen, was developed. The proposed method allows for quick and reliable diagnosis of AMD, opening the way for large dataset analysis within population studies and genotype-phenotype correlation analysis.