Distribution of Different Sized Ocular Surface Vessels in Diabetics and Normal Individuals

Detection of signs of disease in external photographs of the eyes via deep learning

Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.

An Analysis of the Areas Occupied by Vessels in the Ocular Surface of Diabetic Patients: An Application of a Nonparametric Tilted Additive Model

(1) Background: As diabetes melllitus (DM) can affect the microvasculature, this study evaluates different clinical parameters and the vascular density of ocular surface microvasculature in diabetic patients. (2) Methods: In this cross-sectional study, red-free conjunctival photographs of diabetic individuals aged 30-60 were taken under defined conditions and analyzed using a Radon transform-based algorithm for vascular segmentation. The Areas Occupied by Vessels (AOV) images of different diameters were calculated. To establish the sum of AOV of different sized vessels. We adopt a novel approach to investigate the association between clinical characteristics as the predictors and AOV as the outcome, that is Tilted Additive Model (TAM). We use a tilted nonparametric regression estimator to estimate the nonlinear effect of predictors on the outcome in the additive setting for the first time. (3) Results: The results show Age (p-value = 0.019) and Mean Arterial Pressure (MAP) have a significant linear effect on AOV (p-value = 0.034). We also find a nonlinear association between Body Mass Index (BMI), daily Urinary Protein Excretion (UPE), Hemoglobin A1C, and Blood Urea Nitrogen (BUN) with AOV. (4) Conclusions: As many predictors do not have a linear relationship with the outcome, we conclude that the TAM will help better elucidate the effect of the different predictors. The highest level of AOV can be seen at Hemoglobin A1C of 9% and AOV increases when the daily UPE exceeds 600 mg. These effects need to be considered in future studies of ocular surface vessels of diabetic patients.

Rubeosis faciei diabeticorum is not associated with oxidative stress and skin autofluorescence

Background

Rubeosis faciei diabeticorum is a persistent facial erythema in patients with diabetes mellitus. The actual pathogenesis has not been studied. However, it is speculated to be a cutaneous diabetic microangiopathy.

Objective

Examine the correlation between the severity of facial erythema and the possible causes of microvascular diabetic complications, namely oxidative stress, hyperglycemia, and cutaneous accumulation of advanced glycation end-products .

Methods

Patients diagnosed with Type 2 diabetes mellitus (n = 32) were enrolled in the study. The facial erythema index was measured using the Mexameter MX18; cutaneous accumulation of advanced glycation end-products was estimated by measuring skin auto fluorescence with the AGE Reader (DiagnOptics Technologies B.V. – Groningen, Netherlands). Glycated haemoglobin, total antioxidant status, and malondialdehyde were measured in blood by TBARS assay. The correlation between the selected variables was assessed by Spearman's rank test; p ≤ 0.05 was considered statistically significant.

Results

There was a statistically significant correlation between total antioxidant status and the facial erythema index (ρ = 0.398, p = 0.024). Malondialdehyde, skin autofluorescence, glycated haemoglobin, body mass index, duration of diabetes, and age did not demonstrate statistically significant correlation with the facial erythema index.

Study limitations

This is an observational study. Elevation of total antioxidant status could have been caused by several factors that might have also influenced the development of rubeosis faciei, including hyperbilirubinemia and hyperuricemia.

Conclusions

The results contradicted expectations. Total antioxidant status correlated positively with facial erythema index; however, there was no correlation with oxidative stress and skin autofluorescence. Further investigations should be conducted to reveal the cause of total antioxidant status elevation in patients with rubeosis faciei.

Functional slit lamp biomicroscopy metrics correlate with cardiovascular risk

PURPOSE

Our aim was to correlate cardiovascular risk factor estimation with bulbar conjunctival blood flow metrics as measured through Functional Slit Lamp Biomicroscopy (FSLB).

METHODS

Cross-sectional study of individuals with otherwise healthy eyelid and corneal anatomy recruited from the Miami Veterans Affairs (VA) Healthcare System eye clinic. We measured conjunctival microvascular hemodynamics by mounting a camera on a slit lamp and cardiovascular risk using the Framingham risk score. Our main outcome measures were correlations between conjunctival vessel parameters (axial and cross-sectional blood flow velocity, blood flow rate) and Framingham score.

RESULTS

We included 84 patients who underwent FSLB. The mean age was 60 years, the majority were male (88%) and approximately half the patients were black (54%). Mean vessel diameter was similar between all Framingham score categories. Axial and cross-sectional blood flow velocities and blood flow rate were lower in individuals with higher Framingham risk score. Specifically, mean cross-sectional blood flow velocity in individuals with a low Framingham risk score was 0.37 ± 0.0.9 mm/s, with an intermediate score was 0.30 ± 0.09 mm/s, and with a high score was 0.29 ± 0.10 mm/s, p = 0.04. Mean blood flow rate in individuals with a low Framingham risk score was 133.4 ± 59.6 pl/s, with an intermediate score was 123.6 ± 39.3 pl/s, and with a high score was 121.9 ± 52.6 pl/s, p = 0.04. The beta coefficient of the blood flow rate for change in Framingham score was -0.73; 95% CI-1.34-0.13, p = 0.02, adjusted for race.

CONCLUSION

FSLB correlates with cardiovascular risk estimation. Future studies should evaluate if FSLB can predict cardiovascular outcomes.