A deep learning approach for the screening of referable age-related macular degeneration - Model development and external validation.

Abstract

Purpose: To develop a deep learning image assessment software, VeriSee™ AMD, and to validate its accuracy in diagnosing referable age-related macular degeneration (AMD).

Methods: For model development, a total of 6801 judgable 45-degree color fundus images from patients, aged 50 years and over, were collected. These images were assessed for AMD severity by ophthalmologists, according to the Age-Related Eye Disease Studies (AREDS) AMD category. Referable AMD was defined as category three (intermediate) or four (advanced). Of these images, 6123 were used for model training and validation. The other 678 images were used for testing the accuracy of VeriSee™ AMD relative to the ophthalmologists. Area under the receiver operating characteristic curve (AUC) for VeriSee™ AMD, and the sensitivities and specificities for VeriSee™ AMD and ophthalmologists were calculated. For external validation, another 937 color fundus images were used to test the accuracy of VeriSee™ AMD.

Results: During model development, the AUC for VeriSee™ AMD in diagnosing referable AMD was 0.961. The accuracy for VeriSee™ AMD for testing was 92.04% (sensitivity 90.0% and specificity 92.43%). The mean accuracy of the ophthalmologists in diagnosing referable AMD was 85.8% (range: 75.93%-97.31%). During external validation, VeriSee AMD achieved a sensitivity of 90.03%, a specificity of 96.44%, and an accuracy of 92.04%.

Conclusions: VeriSee™ AMD demonstrated good sensitivity and specificity in diagnosing referable AMD from color fundus images. The findings of this study support the use of VeriSee™ AMD in assisting with the clinical screening of intermediate and advanced AMD using color fundus photography.