Automated Detection Of Glaucoma Using Retinal Images With Interpretable Deep Learning

Purpose: Our goal is to develop a multi-modal model to automate glaucoma detection accurately using retinal images.Methods: We selected a study cohort from the UK Biobank dataset: healthy (visual acuity 20/30 or better; s (ubjects)= 863, r (etinas)= 1193) and glaucoma (no other ophthalmic conditions; s= 771, r= 1283). The multi-modal model combines multiple deep neural nets (DNN) trained on macular optical coherence tomography (OCT) volumes and color fundus photos (CFP). We also trained three baseline models (BM); BM1 used demographic data (age, gender, ethnicity), BM2 added systemic medical data (cardiovascular, pulmonary), and BM3 added ocular data (IOP, corneal hysteresis, corneal resistance factor). We determined the importance of different features in detecting glaucoma using SHapley Additive exPlanations (SHAP) and integrated gradients. We also evaluated the model on subjects who …