  Diabetes-related retinal conditions can be detected by examining the
posterior of the eye. By contrast, examining the anterior 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; moderate-or-worse DR with an AUC
of 75.3; diabetic macular edema with an AUC of 78.0; and vision-threatening DR
with an AUC of 79.4. 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 predicted top 5% of patients had a 67% chance of having HbA1c > 9%,
and a 20% chance of having vision threatening diabetic retinopathy. The results
generalized to dilated pupils (validation set B, 5,058 patients) and to a
different screening service (validation set C, 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 and patient populations (those without diabetes)
to evaluate its utility for remote diagnosis and management.
