Abstract Body

Diabetes-related retinal conditions can be detected by using a fundoscope or fundus camera to examine the posterior part of the eye. By contrast, examining or imaging the anterior part of the eye can reveal conditions affecting the front of the eye, such as changes to the eyelids, cornea, or crystalline lens. In this work, we studied whether external photographs of the front of the eye can reveal insights into both diabetic retinal diseases and blood glucose control. We developed a deep learning system (DLS) using external eye photographs of 145,832 patients with diabetes from 301 diabetic retinopathy (DR) screening sites in one US state, and evaluated the DLS on three validation sets containing images from 198 sites in 18 other US states. In validation set A (n=27,415 patients, all undilated), the DLS detected poor blood glucose control (HbA1c > 9%) with an area under receiver operating characteristic curve (AUC) of 70.2% (95%CI 69.4-70.9); moderate-or-worse DR with an AUC of 75.3% (95%CI 74.4- 76.2); diabetic macular edema with an AUC of 78.0% (95%CI 76.4-79.6); and vision-threatening DR with an AUC of 79.4% (95%CI 78.1-80.8). For all 4 prediction tasks, the DLS’s AUC was higher (p<0.001) than using available self-reported baseline characteristics (age, sex, race/ethnicity, years with diabetes). In terms of positive predictive value, the top 5% of patients based on DLS-predicted likelihood had a 67% chance of having HbA1c > 9%, and a 20% chance of having vision threatening diabetic retinopathy that needed ophthalmology followup (vs. 54% and 14%, respectively for baseline characteristics). Similarly, the odds ratio per standard deviation increase in the DLS prediction was 2.0 for HbA1c > 9% and 1.6 for vision threatening diabetic retinopathy after adjusting for baseline characteristics (p<0.001 for both). The results generalized to patients with dilated pupils in validation set B (n=5,058 patients) and to patients at a different screening service (validation set C, n=10,402 patients). Our results indicate that external eye photographs contain information useful for healthcare providers managing patients with diabetes, and may help prioritize patients for in-person screening. Further work is needed to validate these findings on different devices (e.g., computer web cameras and front-facing smartphone cameras) and patient populations (those without diabetes) to evaluate its utility for remote diagnosis and management.