Computer-based detection of age-related macular degeneration and glaucoma using retinal images and clinical data

Worldwide, glaucoma and age-related macular degeneration (AMD) cause 12.3% and 8.7% of the cases of blindness and/or vision loss, respectively. According to a 5-year study of Medicare beneficiaries, patients who undergo a regular eye screening, experience less decline of vision than those who had less-frequent examinations. A computer-based screening of retinopathies can be highly cost-effective and efficient; however, most auto-screening software address only one eye disease, limiting their clinical utility and cost-effectiveness. Therefore, we propose a computer-based retinopathy screening system for detection of AMD and glaucoma by integrating information from retinal fundus images and clinical data. First, the retinal image analysis algorithms were developed using Transfer Learning approach to determine presence or absence of the eye disease. The clinical data was then utilized to improve disease detection performance where the image-analysis based algorithms provided sub-optimal classification. The results for binary detection (present/absent) of AMD and Glaucoma were compared with the ground truth provided by a certified retinal reader. We applied the proposed method to a dataset of 304 retinal images with AMD, 299 retinal images with Glaucoma, and 2,341 control retinal images. The algorithms demonstrated sensitivity/specificity of 100%/99.5% for detection of any AMD, 82%/70% for detection of referable AMD, and 75%/81% for detection of referable Glaucoma. The automated detection results agree well with the ground truth suggesting its potential in screening for AMD and Glaucoma.