  External eye photos were recently shown to reveal signs of diabetic retinal
disease and elevated HbA1c. In this paper, we evaluate if external eye photos
contain information about additional systemic medical conditions. We developed
a deep learning system (DLS) that takes external eye photos as input and
predicts multiple systemic parameters, such as those related to the liver
(albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI
creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH);
and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images
from 49,015 patients with diabetes undergoing diabetic eye screening in 11
sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified
systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869
patients with and without diabetes undergoing eye screening in 3 independent
sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared
against baseline models incorporating available clinicodemographic variables
(e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline,
the DLS achieved statistically significant superior performance at detecting
AST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on
validation set A (a patient population similar to the development sets), where
the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B
and C, with substantial patient population differences compared to the
development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by
7.3-13.2%. Our findings provide further evidence that external eye photos
contain important biomarkers of systemic health spanning multiple organ
systems. Further work is needed to investigate whether and how these biomarkers
can be translated into clinical impact.
