The Fujian eye cross sectional study: objectives, design, and general characteristics

Association between long-term exposure to air pollution and diabetic retinopathy: Evidence from the Fujian Eye Study

BACKGROUND

Diabetic retinopathy (DR), one of the most common microvascular complications of diabetes mellitus (DM), is a major contributor of vision impairment and blindness worldwide. Studies have shown that air pollution exposure is adversely associated with DM. However, evidence is scarce regarding how air pollution exposure affects DR. This study aimed to investigate the association between ambient air pollution exposure and DR risk.

METHODS

The study population was based on the Fujian Eye Study (FJES), an ophthalmologic, epidemiologic survey investigating the eye health condition of residents in Fujian Province from 2018 to 2019. Daily average concentrations of ambient air pollutants (PM2.5, PM10, SO2, NO2, and O3) were acquired from a high-resolution air quality dataset in China from 2013 to 2018. We used a logistic regression model to examine the associations between DR risk and long-term air pollution at various exposure windows.

RESULTS

A total of 2405 out of the 8211 participants were diagnosed with diabetes, among whom 183 had DR. Ambient air pollution, especially particulate matter (i.e., PM2.5 and PM10) and NO2 were positively associated with DR prevalence among all the study subjects. Ambient SO2 and O3 concentrations were not associated with DR prevalence. PM2.5 and NO2 seemed to be borderline significantly associated with increased prevalence of DR in subjects with DM, especially under the model adjusted for sex, age, BMI, SBP, and DBP.

CONCLUSIONS

These findings showed that long-term exposure to ambient particulate matter and NO2 was associated with a high DR risk in Fujian province, where ambient air pollution is relatively low.

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening. Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed. Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors' presented relatively large AUC (0.891-0.997), high sensitivity (87.65-100%), and high specificity (80.12-99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors' compared to senior and junior ophthalmologists (p < 0.05). Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening.