Purpose: Patients with Type II Diabetes are at increased risk Diabetic Retinopathy (DR). Early detection and treatment of DR is critical for saving vision and preventing blindness. To address the need for increased DR detection and referrals, we evaluated the use of artificial intelligence (AI) for screening DR. Methods: Patient images were obtained using a 45-degree Canon Non-Mydriatic CR-2 Plus AF retinal camera in the Department of Endocrinology Clinic (Newark, NJ) and in a community screening event (Newark, NJ). Images were initially classified by an on-site grader and uploaded for analysis by EyeArt, a cloud-based AI software developed by Eyenuk (California, USA). The images were also graded by an off-site retina specialist. Using Fleiss kappa analysis, a correlation was investigated between the three grading systems, the AI, onsite grader, and a US board-certified retina specialist, for a diagnosis of DR and referral pattern. Results: The EyeArt results, onsite grader, and the retina specialist had a 79% overall agreement on the diagnosis of DR. The kappa value for concordance on a diagnosis was 0.69 (95% CI: 0.61-0.77), indicating substantial agreement. Referral patterns by EyeArt, the onsite grader, and the ophthalmologist had an 85% overall agreement. The kappa value for concordance on “whether to refer” was 0.70 (95% CI: 0.60-0.80), indicating substantial agreement. Conclusions: This retrospective cross-sectional analysis offers insights into use of AI in diabetic screenings and the significant role it will play in automated detection of DR. The EyeArt readings were beneficial with some limitations in a community screening environment.

Purpose We sought to describe leisure-time, aerobic, and muscle strengthening physical activity (PA) patterns in U.S. Asian Indians, in comparison to other races/ethnicities. Design, Setting, and Sample We utilized the 2011–2018 National Health Interview Surveys, a set of cross-sectional, nationally representative surveys of the U.S. noninstitutionalized population. Our study population included 257 652 adults who answered PA questions. Measures PA was classified per 2008 U.S. guidelines and continuously per estimated metabolic equivalents (METs). Race was classified into White, Black, Asian Indian, Other Asian, and American Indian/Alaskan Native/Multiracial. Analysis We used survey design-adjusted, multivariable logistic regression to determine odds of sufficient and highly active physical activity levels, adjusting for predisposing, enabling, need, and health care service factors as guided by the Anderson Model. We also used linear regression to determine racial differences in average MET-minutes/week. Analysis was additionally stratified by comorbidity status. Results While Asian Indians (N = 3049) demonstrated similar odds of sufficient aerobic PA as Whites (aOR [95% CI]: .97 [.88,1.07]), Asian Indians had 22% lower odds of meeting highly active aerobic PA levels (.78 [.71,0.87]) and 18% lower odds of meeting sufficient muscle strengthening PA levels (.82 [.73,0.91]). This translated to an average 172 (95% CI: 45 300) fewer MET-minutes. Furthermore, this decrease in MET-minutes/week was especially apparent in those without hypertension (β[95% CI]: −164 [-314,-15]) without diabetes (−185 [-319,-52]), and low/normal BMI (−422 [-623,-222]). Conclusion Asian Indians, especially those without comorbidities, are less likely to engage in high-intensity physical activity than Whites.

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